Working with Geospatial Hydrologic Data for Watershed Analyses in R and Python Using Web Services
Introduction
- Housekeeping
- Introductions
- Agenda:
- Key Building blocks in R and Python
- Examples of fundamental Tools in R
- Examples of fundamental Tools in Python
- Use Cases and Worked Examples
- Demonstration of Watershed Analysis with
LeafmapandWhiteboxTools(leafmap.org/workshops/ICRW_2023/)
- Framework:
This document and all of the workshop material are available at this GitHub repository:
- https://github.com/mhweber/ICRW8_Geospatial_Workshop
To Follow Along with the Code
- This document is built in RStudio with
Quartoand rendered to html - all code sections can be run in RStudio in the code blocks inQuarto, or copied and pasted into a new R script document - If you are familiar with using git and GitHub, you can fork and clone the repository and run locally - if not, you can simply copy and paste code to follow along with examples
- Python code examples are in jupyter notebooks
- if you have successfully set up an environment on your local machine you can run notebooks there
- alternatively, you can use the
binderlink we have set up to run jupyter notebooks in a binder environment
The views expressed in this presentation are those of the authors and do not necessarily represent the views or policies of the U.S. Environmental Protection Agency or the U.S.G.S.
This land is ancestral and unceded land of the Chepenafa (Mary’s River) Band of the Kalapuya (pronounce “cal-uh-poo-yuh”), who have stewarded this beautiful area for generations. The descendants of the Kalapuya People are now part of the federally recognized Confederated Tribes of Grand Ronde and Confederated Tribes of the Siletz of Oregon. The peak you see from numerous vantage points around Corvallis is Mary’s Peak, which the Kalapuya called tcha Timanwi, or ‘place of spiritual power’, and is the highest peak in the Oregon Coast Range (4,097 feet).
Key Concepts Across Both languages
Core Libraries
- Spatial data structures across languages and applications is primarily organized through OSgeo and OGC) and a few core libraries underpin spatial libraries in programming languages and in software applications(R, Python, QGIS, ArcPro)!
These libraries include:
PROJ –> Spatial projections, transformations
GEOS –> Geometry operations (measures, relations)
GDAL –> Raster and feature abstraction and processing (read, write)
NetCDF –> Multidimensional (XYZT) data abstraction (read, write)
Requesting web services (APIs)
More and more data is available as web services, and the data is stored in a remote server in JSON, XML, HTML format which is accessible through API’s.
JSON (JavaScript Object Notation) is a lightweight data-interchange format which is easy for machines to generate and parse; easy for humans to write and read.
Sample JSON format : { “data”: “Click Here”, “size”: 36, “style”: “bold”, “name”: “text1”, }
API - An Application Programming Interface (API) takes a structured request from an application and returns structured results from the host application. With a Rest API you’re getting a representation of the requested data stored in a server. A Rest API also is what we call ‘stateless’, which means a server doesn’t store any data between requests from clients.
Rest APIs access data through uniform resource identifiers (URIs), which are essentially a string of characters that identify a specific resource. The type of URI used by a Rest API is a uniform resource locator (URL).
HTTP clients are used for accessing the API. HyperText Transfer Protocol (HTTP) enables communication between the client and server using HTTP methods. If we want to access or manipulate resources a Rest API uses specific request verbs we need to become familiar with:
GET: used to acquire data from a database
POST: used to add data to a database
PUT: update the data in a database
DELETE: delete data in a database
Geospatial data representation fundamentals: simple features and geospatial grids
*Vector data are comprised of points, lines, and polygons that represent discrete spatial entities, such as a river, watershed, or stream gauge.
Raster data divides spaces into rectilinear cells (pixels) to represent spatially continuous phenomena, such as elevation or the weather. The cell size (or resolution) defines the fidelity of the data.
For Vector data Simple Features (officially Simple Feature Access) is both an OGC and International Organization for Standardization (ISO) standard that specifies how (mostly) two-dimensional geometries can represent and describe objects in the real world. Simple features includes:
- a class hierarchy
- a set of operations
- binary and text encodings
It describes how such objects can be stored in and retrieved from databases, and which geometrical operations should be defined for them.
It outlines how the spatial elements of POINTS (XY locations with a specific coordinate reference system) extend to LINES, POLYGONS and GEOMETRYCOLLECTION(s).
The “simple” adjective also refers to the fact that the line or polygon geometries are represented by sequences of points connected with straight lines that do not self-intersect.
Simple and valid geometries and ring direction
This breakdown of simple features follows for the most part this section in Spatial Data Science For linestrings to be considered simple they must not self-intersect:
Code
library(sf)Linking to GEOS 3.11.1, GDAL 3.6.2, PROJ 9.1.1; sf_use_s2() is TRUE
Code
(ls <- st_linestring(rbind(c(0,0), c(1,1), c(2,2), c(0,2), c(1,1), c(2,0))))LINESTRING (0 0, 1 1, 2 2, 0 2, 1 1, 2 0)
is_simple
FALSE
For polygons several other conditions have to be met to be simple:
- polygon rings are closed (the last point equals the first)
- polygon holes (inner rings) are inside their exterior ring
- polygon inner rings maximally touch the exterior ring in single points, not over a line
- a polygon ring does not repeat its own path
- in a multi-polygon, an external ring maximally touches another exterior ring in single points, not over a line
z and m coordinates As well as having the necessary X and Y coordinates, single point (vertex) simple features can have:
- a Z coordinate, denoting altitude, and/or
- an M value, denoting some “measure”
Text and binary encodings A key part of the standard feature encoding is text and binary encodings. The well-known text (WKT) encoding we have shown above gives us a human-readable description of the geometry. The well-known binary (WKB) encoding is machine-readable, lossless, and faster to work with than text encoding. WKB is used for all interactions with GDAL and GEOS.
Operations on geometries We can break down operations on geometries for vector features in the following way:
- predicates: a logical asserting a certain property is
TRUE - measures: a quantity (a numeric value, possibly with measurement unit)
- transformations: newly generated geometries
We can look at these operations by what they operate on, whether the are single geometries, pairs, or sets of geometries:
- unary when it’s a single geometry
- binary when it’s pairs of geometries
- n-ary when it’s sets of geometries
Unary predicates work to describe a property of a geometry.
A list of unary predicates:
| predicate | meaning |
|---|---|
is |
Tests if geometry belongs to a particular class |
is_simple |
Tests whether geometry is simple |
is_valid |
Test whether geometry is valid |
is_empty |
Tests if geometry is empty |
A list of binary predicates is:
| predicate | meaning | inverse of |
|---|---|---|
contains |
None of the points of A are outside B | within |
contains_properly |
A contains B and B has no points in common with the boundary of A | |
covers |
No points of B lie in the exterior of A | covered_by |
covered_by |
Inverse of covers |
|
crosses |
A and B have some but not all interior points in common | |
disjoint |
A and B have no points in common | intersects |
equals |
A and B are topologically equal: node order or number of nodes may differ; identical to A contains B and A within B | |
equals_exact |
A and B are geometrically equal, and have identical node order | |
intersects |
A and B are not disjoint | disjoint |
is_within_distance |
A is closer to B than a given distance | |
within |
None of the points of B are outside A | contains |
touches |
A and B have at least one boundary point in common, but no interior points | |
overlaps |
A and B have some points in common; the dimension of these is identical to that of A and B | |
relate |
Given a mask pattern, return whether A and B adhere to this pattern |
See the Geometries chapter of Spatial Data Science for a full treatment that also covers **unary and binary measures* as well as unary, binary and n-ary transformers
Raster data model
You can read the GDAL raster data model and OpenGIS Grid Coverages specification but with a raster data model (a cell-based tesselation) you can read / write and operate on raster data in numerous formats using gdal:
Drainage basins and catchments - mainstems and flowpaths
When writing software for hydrologic data, concrete conceptual and logical definitions for the spatial features our software works with are critical to enabling interoperability. Along these lines, some key definitions to consider:
- Catchment: A physiographic unit with zero or one inlets and one outlet. A catchment is represented by one or more partial realizations; flowpath, divide, and networks of flowpaths and divides.
- Nexus: Conceptual outlet for water contained by a catchment. The hydro nexus concept represents the place where a catchment interacts with another catchment.
- Flowpath: A flowpath is a linear geometry that represents the connection between a catchment’s inlet and its outlet. All flowpaths have a local drainage area and may be aggregates of flowlines.
- Flowline: A flowline is a linear geometry that represents a segment of a flowing body of water. Some flowlines have no local drainage area and are never aggregate features.
- Drainage Basin: A catchment with zero inlets and one (internal or external) outlet.
- Mainstem: The flowpath of a drainage basin.
In the following, we create a representation of catchment divides and flowpaths with the hydrologic locations that represent their nexuses.
Note: both the divide and flowpath are said to be realizations of the overall catchment that they represent. In other words, the divide and flowpath are both part of the representation of a catchment.
Code
source(system.file("extdata/new_hope_data.R", package = "nhdplusTools"))
catchment <- sf::st_geometry(new_hope_catchment)
flowpath <- sf::st_geometry(dplyr::filter(new_hope_flowline, COMID %in% new_hope_catchment$FEATUREID))
nexus <- sf::st_geometry(nhdplusTools::get_node(flowpath, "end"))
plot_window <- sf::st_as_sfc(sf::st_bbox(dplyr::filter(new_hope_catchment, FEATUREID %in% c(8891178, 8895564))))
par(mar = c(0,0,0,0))
plot(plot_window, col = NA, border = NA)
plot(catchment, col = NA, border = "grey25", add = TRUE)
plot(flowpath, col = "dodgerblue3", add = TRUE)
plot(nexus, col = "springgreen", add = TRUE, pch = 20)In this example, we create a drainage basin boundary (divide), mainstem flowpath of the drainage basin, and the flowlines that make up the hydrographic network of the drainage basin.
Fun Fact: the word “watershed” has come to refer to the land encompassed by a drainage divide but this usage is not correct. A watershed is “a dividing ridge between drainage areas” which is in line with the non hydrologic use of the word: a crucial dividing point, line, or factor.
Code
basin <- sf::st_geometry(sf::st_union(new_hope_catchment, by_feature = FALSE))
flowline <- sf::st_geometry(new_hope_flowline)
mainstem <- sf::st_geometry(new_hope_flowline[new_hope_flowline$LevelPathI == min(new_hope_flowline$LevelPathI),])
par(mar = c(0,0,0,0))
plot(basin, lwd = 2)
plot(flowline, col = "dodgerblue", add = TRUE)
plot(mainstem, col = "blue", lwd = 2, add = TRUE)Demonstration of Key Concepts in Each Language
- drainage basins and catchments - mainstems and flowpaths
- geospatial data representation fundamentals, simple features and geospatial grids
Examples in R
For more detiled treatment see material for US EPA R User Group 2021 workshop
sf: simple features
In R, the sf package provides “support for simple features, a standardized way to encode spatial vector data…. [and] Binds to ‘GDAL’ for reading and writing data, to ‘GEOS’ for geometrical operations, and to ‘PROJ’ for projection conversions and datum transformations.”
When using R, you’ are’re using an interface to the core community standards, software, and practices (this isn’t exclusive to R). TO highlight this we can install (do this once) and attach sf to view the external dependencies versions of the libraries linked to sf.
Code
# install.packages("sf")
library(sf)
sf_extSoftVersion() GEOS GDAL proj.4 GDAL_with_GEOS USE_PROJ_H
"3.11.1" "3.6.2" "9.1.1" "true" "true"
PROJ
"9.1.1"
The bindings to these lower-level C libraries, and, the larger sf ecosystem in R can be seen below:
In the sf implementation in the R ecosystem stores simple feature geometries (sfg) as part of a larger data.frame using a simple feature geometry list-column (sfg). The collection of attribute and spatial information define a simple feature that can be operated on in both table (SQL) and spatial (GEOS, etc) contexts. Not only does this allow us to make the most use of the growing spatial community but also of the growing data science community (see ggplot, dplyr, data.table, dbplyr, arrow, etc.)
In practice, an sf object in R looks like the following:
We can break this down following examples presented in the recently published Spatial Data Science by Edzar Pebesma and Rober Bivand. The most common simple feature geometries used to represent a single feature are:
| type | description |
|---|---|
POINT |
single point geometry |
MULTIPOINT |
set of points |
LINESTRING |
single linestring (two or more points connected by straight lines) |
MULTILINESTRING |
set of linestrings |
POLYGON |
exterior ring with zero or more inner rings, denoting holes |
MULTIPOLYGON |
set of polygons |
GEOMETRYCOLLECTION |
set of the geometries above |
Code
library(sf) |> suppressPackageStartupMessages()
par(mfrow = c(2,4))
par(mar = c(1,1,1.2,1))
# 1
p <- st_point(0:1)
plot(p, pch = 16)
title("point")
box(col = 'grey')
# 2
mp <- st_multipoint(rbind(c(1,1), c(2, 2), c(4, 1), c(2, 3), c(1,4)))
plot(mp, pch = 16)
title("multipoint")
box(col = 'grey')
# 3
ls <- st_linestring(rbind(c(1,1), c(5,5), c(5, 6), c(4, 6), c(3, 4), c(2, 3)))
plot(ls, lwd = 2)
title("linestring")
box(col = 'grey')
# 4
mls <- st_multilinestring(list(
rbind(c(1,1), c(5,5), c(5, 6), c(4, 6), c(3, 4), c(2, 3)),
rbind(c(3,0), c(4,1), c(2,1))))
plot(mls, lwd = 2)
title("multilinestring")
box(col = 'grey')
# 5 polygon
po <- st_polygon(list(rbind(c(2,1), c(3,1), c(5,2), c(6,3), c(5,3), c(4,4), c(3,4), c(1,3), c(2,1)),
rbind(c(2,2), c(3,3), c(4,3), c(4,2), c(2,2))))
plot(po, border = 'black', col = '#ff8888', lwd = 2)
title("polygon")
box(col = 'grey')
# 6 multipolygon
mpo <- st_multipolygon(list(
list(rbind(c(2,1), c(3,1), c(5,2), c(6,3), c(5,3), c(4,4), c(3,4), c(1,3), c(2,1)),
rbind(c(2,2), c(3,3), c(4,3), c(4,2), c(2,2))),
list(rbind(c(3,7), c(4,7), c(5,8), c(3,9), c(2,8), c(3,7)))))
plot(mpo, border = 'black', col = '#ff8888', lwd = 2)
title("multipolygon")
box(col = 'grey')
# 7 geometrycollection
gc <- st_geometrycollection(list(po, ls + c(0,5), st_point(c(2,5)), st_point(c(5,4))))
plot(gc, border = 'black', col = '#ff6666', pch = 16, lwd = 2)
title("geometrycollection")
box(col = 'grey')Code
pPOINT (0 1)
Code
mpMULTIPOINT ((1 1), (2 2), (4 1), (2 3), (1 4))
Code
lsLINESTRING (1 1, 5 5, 5 6, 4 6, 3 4, 2 3)
Code
mlsMULTILINESTRING ((1 1, 5 5, 5 6, 4 6, 3 4, 2 3), (3 0, 4 1, 2 1))
Code
poPOLYGON ((2 1, 3 1, 5 2, 6 3, 5 3, 4 4, 3 4, 1 3, 2 1), (2 2, 3 3, 4 3, 4 2, 2 2))
Code
mpoMULTIPOLYGON (((2 1, 3 1, 5 2, 6 3, 5 3, 4 4, 3 4, 1 3, 2 1), (2 2, 3 3, 4 3, 4 2, 2 2)), ((3 7, 4 7, 5 8, 3 9, 2 8, 3 7)))
Code
gcGEOMETRYCOLLECTION (POLYGON ((2 1, 3 1, 5 2, 6 3, 5 3, 4 4, 3 4, 1 3, 2 1), (2 2, 3 3, 4 3, 4 2, 2 2)), LINESTRING (1 6, 5 10, 5 11, 4 11, 3 9, 2 8), POINT (2 5), POINT (5 4))
This extends the idea of “tidy” data in that each row represents one observation, which has one geometric representation of the real world feature it describes.
An example of basic use:
Code
# Define file path
filename <- system.file("shape/nc.shp", package="sf")
# read in file
(nc <- read_sf(filename))Simple feature collection with 100 features and 14 fields
Geometry type: MULTIPOLYGON
Dimension: XY
Bounding box: xmin: -84.32385 ymin: 33.88199 xmax: -75.45698 ymax: 36.58965
Geodetic CRS: NAD27
# A tibble: 100 × 15
AREA PERIMETER CNTY_ CNTY_ID NAME FIPS FIPSNO CRESS_ID BIR74 SID74 NWBIR74
<dbl> <dbl> <dbl> <dbl> <chr> <chr> <dbl> <int> <dbl> <dbl> <dbl>
1 0.114 1.44 1825 1825 Ashe 37009 37009 5 1091 1 10
2 0.061 1.23 1827 1827 Alle… 37005 37005 3 487 0 10
3 0.143 1.63 1828 1828 Surry 37171 37171 86 3188 5 208
4 0.07 2.97 1831 1831 Curr… 37053 37053 27 508 1 123
5 0.153 2.21 1832 1832 Nort… 37131 37131 66 1421 9 1066
6 0.097 1.67 1833 1833 Hert… 37091 37091 46 1452 7 954
7 0.062 1.55 1834 1834 Camd… 37029 37029 15 286 0 115
8 0.091 1.28 1835 1835 Gates 37073 37073 37 420 0 254
9 0.118 1.42 1836 1836 Warr… 37185 37185 93 968 4 748
10 0.124 1.43 1837 1837 Stok… 37169 37169 85 1612 1 160
# ℹ 90 more rows
# ℹ 4 more variables: BIR79 <dbl>, SID79 <dbl>, NWBIR79 <dbl>,
# geometry <MULTIPOLYGON [°]>
Code
# Map
plot(nc['SID79'])Code
# Spatial measures!
head(st_area(nc))Units: [m^2]
[1] 1137107793 610916077 1423145355 694378925 1520366979 967504822
Code
# Spatial operations!
{
st_union(nc) |> plot()
st_centroid(nc)$geometry |> plot(col = "red", add = TRUE)
}Warning: st_centroid assumes attributes are constant over geometries
Code
# data science operations
library(dplyr)
Attaching package: 'dplyr'
The following objects are masked from 'package:terra':
intersect, union
The following objects are masked from 'package:stats':
filter, lag
The following objects are masked from 'package:base':
intersect, setdiff, setequal, union
Code
{
plot(nc$geometry)
plot(slice_max(nc, AREA, n = 10)$geometry,
col = "red", add = TRUE)
plot(slice_max(nc, AREA, n =5)$geometry,
col = "yellow", add = TRUE)
plot(slice_max(nc, AREA, n = 1)$geometry,
col = "green", add = TRUE)
}Measure (GEOS Measures)
Measures are the questions we ask about the dimension of a geometry, once a coordinate reference system has been established and we can compute: - How long is a line or polygon perimeter? (unit) - What is the area of a polygon? *(unit^2) - How far are two objects from one another? (unit)
- Measures come from the GEOS library
- Measures are calculated in the units of the projection
We’ll demonstrate with a toy dataset of USGS gages in a data package from a previous workshop:
Code
# remotes::install_github("mhweber/awra2020spatial")
gages <- read.csv(system.file("extdata", "Gages_flowdata.csv", package = "awra2020spatial")) |>
dplyr::select(SOURCE_FEA, STATE, LAT_SITE, LON_SITE)Rows: 2,771
Columns: 4
$ SOURCE_FEA <int> 14361500, 14344500, 10378500, 14341500, 14343000, 13092500,…
$ STATE <chr> "OR", "OR", "OR", "OR", "OR", "ID", "OR", "OR", "OR", "OR",…
$ LAT_SITE <dbl> 42.43040, 42.42763, 42.42488, 42.40819, 42.40263, 42.39648,…
$ LON_SITE <dbl> -123.3178, -122.6011, -119.9233, -122.6011, -122.5373, -114…
Using st_as_sf in sf we can easily make a data frame with coordinates into a simple features data frame
Code
gages <- gages |>
sf::st_as_sf(coords = c("LON_SITE", "LAT_SITE"), crs = 4269)Rows: 2,771
Columns: 3
$ SOURCE_FEA <int> 14361500, 14344500, 10378500, 14341500, 14343000, 13092500,…
$ STATE <chr> "OR", "OR", "OR", "OR", "OR", "ID", "OR", "OR", "OR", "OR",…
$ geometry <POINT [°]> POINT (-123.3178 42.4304), POINT (-122.6011 42.42763)…
What is the distance from the first stream gage to the second stream gage?
Code
sf::st_distance(gages[1,], gages[2,])Units: [m]
[,1]
[1,] 58822.95
We can generate toy area by buffering a point (to make a polygon) and find area and perimeter. We need to project into a planar CRS to get perimeter so we will project to an Albers projection using an epsg code:
Code
poly <- sf::st_buffer(gages[1,],200)
sf::st_area(poly)127234.3 [m^2]
Code
poly <- sf::st_transform(poly, 5070)
lwgeom::st_perimeter(poly)1713.517 [m]
terra for raster data
We’ll look at using terra for working with raster data in R - stars is another library for working with raster data and spatiotemporal arrays (raster and vector data cubes) but for the sake of time we won’t demonstrate stars in this workshop.
terra builds on the older raster package and provides methods for low-level data manipulation as well as high-level global, local, zonal, and focal computations. The predict and interpolate methods facilitate the use of regression type (interpolation, machine learning) models for spatial prediction, including with satellite remote sensing data. Processing of very large files is supported.
Like sf, terra links to GDAL, and the version and drivers can be viewed using package support functions:
Code
# install.packages(terra)
library(terra)
gdal()[1] "3.6.2"
Code
DT::datatable(gdal(drivers = TRUE))We can get raster data the National Map family of APIs and in particular 3DEP Elevation data and work with it using terra or stars. We’ll use Mike Johnson’s AOI package to get a county polygon to use as a template to pass to terrainr for our area of interest
Code
library(terrainr)
# remotes::install_github("mikejohnson51/AOI")
library(AOI)The legacy packages maptools, rgdal, and rgeos, underpinning this package
will retire shortly. Please refer to R-spatial evolution reports on
https://r-spatial.org/r/2023/05/15/evolution4.html for details.
This package is now running under evolution status 0
Code
library(mapview)
mapviewOptions(fgb=FALSE)
AOI::aoi_get(list("Corvallis, OR", 10, 10)) |> mapview()Code
# aoi <- AOI::aoi_get(state = "OR", county = "Benton")
aoi <- aoi_get(list("Corvallis, OR", 10, 10))
output_tiles <- get_tiles(aoi,
services = c("elevation", "ortho"),
resolution = 30 # pixel side length in meters
)Code
terra::plot(terra::rast(output_tiles[["elevation"]][[1]]))Code
terra::plotRGB(terra::rast(output_tiles[["ortho"]][[1]]),scale = 1)We can also use the [elevatr] package by Jeff Hollister for accessing elevation data through web services and clip out elevation to a watershed basin we access through nhdplusTools and the NLDI.
Note that get_elev_raster from elevatr will generate a set of warnings, particularly regarding retirement of rgdal - the package will need to be updated to conform with retirement of rgdal, rgeos and maptools.
Code
library(nhdplusTools)
library(elevatr)
library(mapview)
mapviewOptions(fgb=FALSE)
# We're passing an identifier for the stream reach (and catchment) that we know is the downstream-most segment on the Calapooia River using the COMID below
start_comid = 23763529
nldi_feature <- list(featureSource = "comid", featureID = start_comid)
basin <- nhdplusTools::get_nldi_basin(nldi_feature = nldi_feature)
x <- get_elev_raster(basin, z = 12)
# x is returned as a raster object rather than a terra spatraster, so we need to convert to spatraster
mapview::mapview(basin) + mapview(x)Warning in rasterCheckSize(x, maxpixels = maxpixels): maximum number of pixels for Raster* viewing is 5e+05 ;
the supplied Raster* has 19843980
... decreasing Raster* resolution to 5e+05 pixels
to view full resolution set 'maxpixels = 19843980 '
We can also crop the elevation raster to our basin using terra - note that mapview expects raster objects rather than terra SpatRasters so we need to convert between terr and raster.
Code
library(raster)Loading required package: sp
Attaching package: 'raster'
The following object is masked from 'package:dplyr':
select
Code
x <- terra::mask(terra::rast(x), basin)
x <- raster::raster(x)
mapview::mapview(basin, alpha.regions = 0.02, color='blue', lwd=2) + mapview(x)Warning in rasterCheckSize(x, maxpixels = maxpixels): maximum number of pixels for Raster* viewing is 5e+05 ;
the supplied Raster* has 19843980
... decreasing Raster* resolution to 5e+05 pixels
to view full resolution set 'maxpixels = 19843980 '
httr and jsonlite
In R, httr and jsonlite packages are used to consume the API’s that provide data in json format.
httr httr provides us with an HTTP client to access the API with GET/POST methods, passing query parameters, and verifying the response with regard to the data format.
jsonlite This R package is used to convert the received json format to readable R object or data frame. jsonlite can also be used to convert R objects or a data frame to a json format data type.
Along with these two packages, rlist in R can be used to perform additional manipulation on the received json response. list.stack and list.select are two important methods exposed by rlist that can be used to get parsed json data into a tibble.
To learn about these (and other) packages, type ?httr, ?jsonlite and ?rlist in the console of Rstudio to view the documentation.
Here we see an example using a JSON API from the world bank that uses a GET request below - we can see a couple ways of passing request parameters - first we’ll embed the query directly in the URL for the request itself:
Code
library(httr)
library(jsonlite)
library(dplyr)
library(data.table)
jsonResponse <- httr::GET("http://api.worldbank.org/country?per_page=10®ion=OED&lendingtype=LNX&format=json")We can also generate the query by passing it in a separate list we create and adding as a query parameter in the GET request
Code
query<-list(per_page="10",region="OED",lendingtype="LNX",format="json")
jsonResponse <- httr::GET("http://api.worldbank.org/country",query=query)The jsonlite package can be used to parse our response which we indicated above to return as json. Why does the attempt below return an error?
Code
df <- jsonlite::fromJSON(jsonResponse)HINT: what type of object is jsonResponse above?
Code
typeof(jsonResponse)[1] "list"
We can also get information about the response using httr:
Code
http_type(jsonResponse)[1] "application/json"
We have a list - let’s take a look to figure out to pull out what we want into a data frame:
Code
names(jsonResponse) [1] "url" "status_code" "headers" "all_headers" "cookies"
[6] "content" "date" "times" "request" "handle"
It seems like content is what we want, how do we pull out?
Code
df <- jsonlite::fromJSON(jsonResponse$content)Still doesn’t work - we need to do some further parsing of this response using content in httr which we can then pass to jsonlite and the fromJSON function to get data into a data frame
Code
jsonResponseParsed <- httr::content(jsonResponse, as="parsed")
is.list(jsonResponseParsed)[1] TRUE
Code
names(jsonResponseParsed[[1]])[1] "page" "pages" "per_page" "total"
Code
names(jsonResponseParsed[[2]])NULL
Code
#Hmmm
is.list(jsonResponseParsed[[2]][[1]]) [1] TRUE
Code
names(jsonResponseParsed[[2]][[1]]) [1] "id" "iso2Code" "name" "region" "adminregion"
[6] "incomeLevel" "lendingType" "capitalCity" "longitude" "latitude"
Code
# now we're getting somewhere...
names(jsonResponseParsed[[2]][[2]]) [1] "id" "iso2Code" "name" "region" "adminregion"
[6] "incomeLevel" "lendingType" "capitalCity" "longitude" "latitude"
We convert the parsed json response list to data table using lapply and rbindlist:
Code
df <- lapply(jsonResponseParsed[[2]],as.data.table)
typeof(df) # still a list - one more step[1] "list"
Code
dt <- rbindlist(df, fill = TRUE)Some APIs like GitHub are easier to pull directly into a dataframe using fromJSON
Code
myGitHubRepos <- fromJSON("https://api.github.com/users/mhweber/repos")
# It returns 79 variables about my repos - we can use select to just get the ones we want to see
myGitHubRepos <- myGitHubRepos |>
dplyr::select(name, stargazers_count, watchers_count, language, has_issues, forks_count)
head(myGitHubRepos) name stargazers_count watchers_count language
1 AccumulationScripts 0 0 Python
2 ArrayTools 0 0 Python
3 awra2020spatial 3 3 R
4 AWRA2022GeoWorkshop 6 6 Jupyter Notebook
5 AWRA_2020_R_Spatial 4 4 HTML
6 AWRA_GIS_R_Workshop 2 2 JavaScript
has_issues forks_count
1 TRUE 0
2 FALSE 0
3 TRUE 0
4 TRUE 5
5 TRUE 2
6 TRUE 3
Examples in Python (separate notebook in binder)
Data access summary
This just builds on examples for Working with Geospatial Hydrologic Data Using Web Services (R) for IOW Webinar
AOI
AOI is NOT on CRAN but provides interfaces for geocoding and retrieving spatial boundaries (e.g state, county, and country). When working with web-based, subsetting services, the ability to quickly define common features is really convenient.
We’ll see through these examples that the concept of AOI is key to requesting subsets of data. Further well see that a reproducible way of generating these is a quic kway to make the sytanx of disparate packages more aligned.
Code
# remotes::install_github("mikejohnson51/AOI")
library(AOI)
# state
aoi_get(state = "OR") |> mapview()Code
# county
aoi_get(state = "OR", county = "Benton") |> mapview()Code
# country
aoi_get(country = "Ukraine") |> mapview()Code
# geocoding
geocode("LaSells Stewart Center Corvallis", pt = TRUE) |> mapview()nhdplusTools
We’ll hear a lot about Dave’s package nhdplusTools - it’s primarily a R package for working with the NHD and the NHD data model. The package covers 5 primary use topics that include:
- data access
- data discovery and subsetting (via web services)
- Indexing and Network Navigation
- Network Navigation
- Network Attributes
Given the focus here on geospatial data via web services, we will look at functions supporting use case 2.
Code
# install.packages('nhdplusTools')
library(nhdplusTools)
grep("get_", ls("package:nhdplusTools"), value = TRUE) [1] "get_boundaries" "get_catchment_characteristics"
[3] "get_characteristics_metadata" "get_DD"
[5] "get_DM" "get_elev_along_path"
[7] "get_flowline_index" "get_gagesII"
[9] "get_hr_data" "get_huc"
[11] "get_huc12" "get_huc8"
[13] "get_hydro_location" "get_levelpaths"
[15] "get_nhdarea" "get_nhdplus"
[17] "get_nhdplushr" "get_nldi_basin"
[19] "get_nldi_characteristics" "get_nldi_feature"
[21] "get_nldi_index" "get_node"
[23] "get_nwis" "get_partial_length"
[25] "get_path_lengths" "get_path_members"
[27] "get_pathlength" "get_pfaf"
[29] "get_raindrop_trace" "get_sorted"
[31] "get_split_catchment" "get_streamlevel"
[33] "get_streamorder" "get_terminal"
[35] "get_tocomid" "get_UM"
[37] "get_UT" "get_vaa"
[39] "get_vaa_names" "get_vaa_path"
[41] "get_waterbodies" "get_waterbody_index"
[43] "get_wb_outlet" "get_xs_point"
[45] "get_xs_points"
Basic Use
Let’s get data for an AOI around Corvalis. We use AOI::aoi_get to get the OpenStreetMap representation of Corvallis, and initialize an empty list to populate:
Code
corvallis = list()
corvallis$AOI = aoi_get("Corvallis, OR")Populate our Corvallis list with various elements we pull from get_ functions in nhdplusTools:
Code
corvallis$nhdplus <- get_nhdplus(AOI = corvallis$AOI) Spherical geometry (s2) switched off
although coordinates are longitude/latitude, st_intersects assumes that they
are planar
Spherical geometry (s2) switched on
Code
corvallis$waterbodies <- get_waterbodies(AOI = corvallis$AOI) Spherical geometry (s2) switched off
although coordinates are longitude/latitude, st_intersects assumes that they
are planar
Spherical geometry (s2) switched on
Code
corvallis$gages <- get_nwis(AOI = corvallis$AOI)
corvallis$huc12 <- get_huc12(AOI = corvallis$AOI) Warning in get_huc12(AOI = corvallis$AOI): this function is deprecated -- use
get_huc(..., type = "huc12") instead
Spherical geometry (s2) switched off
although coordinates are longitude/latitude, st_intersects assumes that they
are planar
Spherical geometry (s2) switched on
Code
# How many features per entry?
sapply(corvallis, nrow) AOI nhdplus waterbodies gages huc12
1 45 9 2 6
Code
mapview(corvallis)In both the NHDPlus data model, and the emerging international OGC standard for representing surface water features HY_features it’s recognized that hydrologic entities can be “realized” in a number of ways.
For example the holistic notion of a ‘catchment’ can be realized as a ‘divide’, ‘flowpath’, ‘outlet’, or other representation. The nhdplusTools::get_nhdplus functions support getting 3 realizations of the NHD features that are consistent with the NHDPlus Data model (outlet, flowline, catchment, and all).
Code
corvallis <- get_nhdplus(aoi_get("Corvallis, OR"), realization = "all")Spherical geometry (s2) switched off
although coordinates are longitude/latitude, st_intersects assumes that they
are planar
Spherical geometry (s2) switched on
Spherical geometry (s2) switched off
although coordinates are longitude/latitude, st_intersects assumes that they
are planar
Spherical geometry (s2) switched on
Code
mapview(corvallis)dataRetrival
The USGS supported dataRetrieval package provides retrieval functions for USGS and EPA hydrologic and water quality data. Recently a Network Linked Data Index (NLDI) client was also added to the package.
All sorts of dataRetrieval resources: Home page Tutorial for National Water Quality Monditoring Conference - Part 1 Tutorial for National Water Quality Monditoring Conference - Part 2
Basic Use: Get streamflow Data
What were the two gages we picked up geolocating Corvallis?
Code
corvallis$gagesNULL
Code
#install.package('dataRetrieval')
library(dataRetrieval)
library(ggplot2)
# Supply gauge ids from Corvallis example
# Parameter codes can be found with (`dataRetrieval::parameterCdFile`)
# "00060" is stream flow
# we need to bring gages back to corvallis after our last operation
corvallis$AOI = aoi_get("Corvallis, OR")
corvallis$gages <- get_nwis(AOI = corvallis$AOI)
flows = readNWISdv(siteNumbers = corvallis$gages$site_no, parameterCd = "00060") |>
renameNWISColumns()
ggplot(data = flows) +
geom_line(aes(x = Date, y = Flow)) +
facet_wrap('site_no')water quality
We can also get water quality information - see the NWQMC DataRetrieval Tutorial Part 1 for all the details
Code
readWQPdata(statecode = "OR",
countycode = "Benton",
characteristicName = "Phosphorus",
sampleMedia = "Water",
dataProfile = "resultPhysChem") OrganizationIdentifier OrganizationFormalName ActivityIdentifier
1 USGS-OR USGS Oregon Water Science Center nwisor.01.99100409
2 USGS-OR USGS Oregon Water Science Center nwisor.01.99100409
3 USGS-OR USGS Oregon Water Science Center nwisor.01.99301407
4 USGS-OR USGS Oregon Water Science Center nwisor.01.99301254
5 USGS-OR USGS Oregon Water Science Center nwisor.01.99301254
6 USGS-OR USGS Oregon Water Science Center nwisor.01.99300804
7 USGS-OR USGS Oregon Water Science Center nwisor.01.99400519
8 USGS-OR USGS Oregon Water Science Center nwisor.01.99400519
9 USGS-OR USGS Oregon Water Science Center nwisor.01.99400287
10 USGS-OR USGS Oregon Water Science Center nwisor.01.99400287
11 USGS-OR USGS Oregon Water Science Center nwisor.01.99400165
12 USGS-OR USGS Oregon Water Science Center nwisor.01.99400165
13 USGS-OR USGS Oregon Water Science Center nwisor.01.01200407
14 USGS-OR USGS Oregon Water Science Center nwisor.01.01200391
15 USGS-OR USGS Oregon Water Science Center nwisor.01.99400143
16 USGS-OR USGS Oregon Water Science Center nwisor.01.99400143
17 USGS-OR USGS Oregon Water Science Center nwisor.01.99400160
18 USGS-OR USGS Oregon Water Science Center nwisor.01.99400160
19 USGS-OR USGS Oregon Water Science Center nwisor.01.99400514
20 USGS-OR USGS Oregon Water Science Center nwisor.01.99400514
21 USGS-OR USGS Oregon Water Science Center nwisor.01.99400513
22 USGS-OR USGS Oregon Water Science Center nwisor.01.99400513
23 USGS-OR USGS Oregon Water Science Center nwisor.01.99400161
24 USGS-OR USGS Oregon Water Science Center nwisor.01.99400161
25 USGS-OR USGS Oregon Water Science Center nwisor.01.99400517
26 USGS-OR USGS Oregon Water Science Center nwisor.01.99400517
27 USGS-OR USGS Oregon Water Science Center nwisor.01.99400349
28 USGS-OR USGS Oregon Water Science Center nwisor.01.99400349
29 USGS-OR USGS Oregon Water Science Center nwisor.01.99400508
30 USGS-OR USGS Oregon Water Science Center nwisor.01.99400508
31 USGS-OR USGS Oregon Water Science Center nwisor.01.99400524
32 USGS-OR USGS Oregon Water Science Center nwisor.01.99400524
33 USGS-OR USGS Oregon Water Science Center nwisor.01.99400516
34 USGS-OR USGS Oregon Water Science Center nwisor.01.99400516
35 USGS-OR USGS Oregon Water Science Center nwisor.01.99400509
36 USGS-OR USGS Oregon Water Science Center nwisor.01.99400509
37 USGS-OR USGS Oregon Water Science Center nwisor.01.99600036
38 USGS-OR USGS Oregon Water Science Center nwisor.01.99600044
39 USGS-OR USGS Oregon Water Science Center nwisor.01.00700845
40 USGS-OR USGS Oregon Water Science Center nwisor.01.00700845
41 USGS-OR USGS Oregon Water Science Center nwisor.01.00900533
42 USGS-OR USGS Oregon Water Science Center nwisor.01.01200391
43 USGS-OR USGS Oregon Water Science Center nwisor.01.01200390
44 USGS-OR USGS Oregon Water Science Center nwisor.01.01200390
45 USGS-OR USGS Oregon Water Science Center nwisor.01.99800161
46 USGS-OR USGS Oregon Water Science Center nwisor.01.99800161
47 USGS-OR USGS Oregon Water Science Center nwisor.01.01300012
48 USGS-OR USGS Oregon Water Science Center nwisor.01.01300013
49 USGS-OR USGS Oregon Water Science Center nwisor.01.01300013
50 USGS-OR USGS Oregon Water Science Center nwisor.01.01200904
51 USGS-OR USGS Oregon Water Science Center nwisor.01.99800156
52 USGS-OR USGS Oregon Water Science Center nwisor.01.99800156
53 USGS-OR USGS Oregon Water Science Center nwisor.01.99800158
54 USGS-OR USGS Oregon Water Science Center nwisor.01.99800164
55 USGS-OR USGS Oregon Water Science Center nwisor.01.99800164
56 USGS-OR USGS Oregon Water Science Center nwisor.01.00500232
57 USGS-OR USGS Oregon Water Science Center nwisor.01.00500219
58 USGS-OR USGS Oregon Water Science Center nwisor.01.01500831
59 USGS-OR USGS Oregon Water Science Center nwisor.01.99800166
60 USGS-OR USGS Oregon Water Science Center nwisor.01.99800166
61 USGS-OR USGS Oregon Water Science Center nwisor.01.00400391
62 USGS-OR USGS Oregon Water Science Center nwisor.01.00400405
63 USGS-OR USGS Oregon Water Science Center nwisor.01.00400406
64 USGS-OR USGS Oregon Water Science Center nwisor.01.00400630
65 USGS-OR USGS Oregon Water Science Center nwisor.01.99800158
66 USGS-OR USGS Oregon Water Science Center nwisor.01.01200407
67 USGS-OR USGS Oregon Water Science Center nwisor.01.00400408
68 USGS-OR USGS Oregon Water Science Center nwisor.01.00500220
69 USGS-OR USGS Oregon Water Science Center nwisor.01.00500231
70 USGS-OR USGS Oregon Water Science Center nwisor.01.01501006
71 USGS-OR USGS Oregon Water Science Center nwisor.01.00500245
72 USGS-OR USGS Oregon Water Science Center nwisor.01.00500230
73 USGS-OR USGS Oregon Water Science Center nwisor.01.99800159
74 USGS-OR USGS Oregon Water Science Center nwisor.01.99800159
75 USGS-OR USGS Oregon Water Science Center nwisor.01.00600311
76 USGS-OR USGS Oregon Water Science Center nwisor.01.00600312
77 USGS-OR USGS Oregon Water Science Center nwisor.01.00600328
78 USGS-OR USGS Oregon Water Science Center nwisor.01.00600315
79 USGS-OR USGS Oregon Water Science Center nwisor.01.00400407
80 USGS-OR USGS Oregon Water Science Center nwisor.01.01500817
81 USGS-OR USGS Oregon Water Science Center nwisor.01.01501103
82 USGS-OR USGS Oregon Water Science Center nwisor.01.01500650
83 USGS-OR USGS Oregon Water Science Center nwisor.01.01500450
84 USGS-OR USGS Oregon Water Science Center nwisor.01.01500921
85 USGS-OR USGS Oregon Water Science Center nwisor.01.01501012
86 USGS-OR USGS Oregon Water Science Center nwisor.01.01300012
87 USGS-OR USGS Oregon Water Science Center nwisor.01.00400465
88 USGS-OR USGS Oregon Water Science Center nwisor.01.01501647
89 USGS-OR USGS Oregon Water Science Center nwisor.01.01500390
90 USGS-OR USGS Oregon Water Science Center nwisor.01.01500733
91 USGS-OR USGS Oregon Water Science Center nwisor.01.01200904
92 USGS-OR USGS Oregon Water Science Center nwisor.01.00600325
93 USGS-OR USGS Oregon Water Science Center nwisor.01.00600329
94 USGS-OR USGS Oregon Water Science Center nwisor.01.01500931
95 USGS-OR USGS Oregon Water Science Center nwisor.01.00400404
96 USGS-OR USGS Oregon Water Science Center nwisor.01.01500601
97 USGS-OR USGS Oregon Water Science Center nwisor.01.01500521
98 USGS-OR USGS Oregon Water Science Center nwisor.01.99800120
99 USGS-OR USGS Oregon Water Science Center nwisor.01.99900018
100 USGS-OR USGS Oregon Water Science Center nwisor.01.99500034
101 USGS-OR USGS Oregon Water Science Center nwisor.01.99500034
102 USGS-OR USGS Oregon Water Science Center nwisor.01.99900022
103 USGS-OR USGS Oregon Water Science Center nwisor.01.99800091
104 USGS-OR USGS Oregon Water Science Center nwisor.01.99900018
105 USGS-OR USGS Oregon Water Science Center nwisor.01.99800089
106 USGS-OR USGS Oregon Water Science Center nwisor.01.99800091
107 USGS-OR USGS Oregon Water Science Center nwisor.01.99800120
108 USGS-OR USGS Oregon Water Science Center nwisor.01.99800116
109 USGS-OR USGS Oregon Water Science Center nwisor.01.99800103
110 USGS-OR USGS Oregon Water Science Center nwisor.01.99800103
111 USGS-OR USGS Oregon Water Science Center nwisor.01.99800101
112 USGS-OR USGS Oregon Water Science Center nwisor.01.99900026
113 USGS-OR USGS Oregon Water Science Center nwisor.01.99800089
114 USGS-OR USGS Oregon Water Science Center nwisor.01.99800101
115 USGS-OR USGS Oregon Water Science Center nwisor.01.99900029
116 USGS-OR USGS Oregon Water Science Center nwisor.01.99900029
117 USGS-OR USGS Oregon Water Science Center nwisor.01.99800109
118 USGS-OR USGS Oregon Water Science Center nwisor.01.99800109
119 USGS-OR USGS Oregon Water Science Center nwisor.01.99800112
120 USGS-OR USGS Oregon Water Science Center nwisor.01.99800112
121 USGS-OR USGS Oregon Water Science Center nwisor.01.99900020
122 USGS-OR USGS Oregon Water Science Center nwisor.01.99900020
123 USGS-OR USGS Oregon Water Science Center nwisor.01.01200171
124 USGS-OR USGS Oregon Water Science Center nwisor.01.01200131
125 USGS-OR USGS Oregon Water Science Center nwisor.01.01200131
126 USGS-OR USGS Oregon Water Science Center nwisor.01.99800092
127 USGS-OR USGS Oregon Water Science Center nwisor.01.99800092
128 USGS-OR USGS Oregon Water Science Center nwisor.01.99800087
129 USGS-OR USGS Oregon Water Science Center nwisor.01.99800097
130 USGS-OR USGS Oregon Water Science Center nwisor.01.99800097
131 USGS-OR USGS Oregon Water Science Center nwisor.01.99800086
132 USGS-OR USGS Oregon Water Science Center nwisor.01.99800100
133 USGS-OR USGS Oregon Water Science Center nwisor.01.99800100
134 USGS-OR USGS Oregon Water Science Center nwisor.01.99800116
135 USGS-OR USGS Oregon Water Science Center nwisor.01.01200171
136 USGS-OR USGS Oregon Water Science Center nwisor.01.99800093
137 USGS-OR USGS Oregon Water Science Center nwisor.01.99800154
138 USGS-OR USGS Oregon Water Science Center nwisor.01.99800154
139 USGS-OR USGS Oregon Water Science Center nwisor.01.99900022
140 USGS-OR USGS Oregon Water Science Center nwisor.01.99800087
141 USGS-OR USGS Oregon Water Science Center nwisor.01.99800117
142 USGS-OR USGS Oregon Water Science Center nwisor.01.99800117
143 USGS-OR USGS Oregon Water Science Center nwisor.01.99800096
144 USGS-OR USGS Oregon Water Science Center nwisor.01.99800096
145 USGS-OR USGS Oregon Water Science Center nwisor.01.99800086
146 USGS-OR USGS Oregon Water Science Center nwisor.01.99800102
147 USGS-OR USGS Oregon Water Science Center nwisor.01.99800102
148 USGS-OR USGS Oregon Water Science Center nwisor.01.99800093
149 USGS-OR USGS Oregon Water Science Center nwisor.01.99900024
150 USGS-OR USGS Oregon Water Science Center nwisor.01.99900024
151 USGS-OR USGS Oregon Water Science Center nwisor.01.99900026
ActivityTypeCode ActivityMediaName
1 Sample-Routine Water
2 Sample-Routine Water
3 Sample-Routine Water
4 Sample-Routine Water
5 Sample-Routine Water
6 Sample-Routine Water
7 Sample-Routine Water
8 Sample-Routine Water
9 Sample-Routine Water
10 Sample-Routine Water
11 Sample-Routine Water
12 Sample-Routine Water
13 Sample-Routine Water
14 Sample-Routine Water
15 Quality Control Sample-Field Replicate Water
16 Quality Control Sample-Field Replicate Water
17 Sample-Routine Water
18 Sample-Routine Water
19 Sample-Routine Water
20 Sample-Routine Water
21 Sample-Routine Water
22 Sample-Routine Water
23 Sample-Routine Water
24 Sample-Routine Water
25 Sample-Routine Water
26 Sample-Routine Water
27 Sample-Routine Water
28 Sample-Routine Water
29 Sample-Routine Water
30 Sample-Routine Water
31 Sample-Routine Water
32 Sample-Routine Water
33 Not determined Water
34 Not determined Water
35 Sample-Routine Water
36 Sample-Routine Water
37 Quality Control Sample-Field Spike Water
38 Quality Control Sample-Field Spike Water
39 Sample-Routine Water
40 Sample-Routine Water
41 Sample-Routine Water
42 Sample-Routine Water
43 Sample-Routine Water
44 Sample-Routine Water
45 Sample-Routine Water
46 Sample-Routine Water
47 Sample-Routine Water
48 Sample-Routine Water
49 Sample-Routine Water
50 Sample-Routine Water
51 Sample-Routine Water
52 Sample-Routine Water
53 Sample-Routine Water
54 Sample-Routine Water
55 Sample-Routine Water
56 Sample-Routine Water
57 Sample-Routine Water
58 Sample-Routine Water
59 Sample-Routine Water
60 Sample-Routine Water
61 Sample-Routine Water
62 Sample-Routine Water
63 Sample-Routine Water
64 Sample-Routine Water
65 Sample-Routine Water
66 Sample-Routine Water
67 Sample-Routine Water
68 Sample-Routine Water
69 Sample-Routine Water
70 Sample-Routine Water
71 Sample-Routine Water
72 Sample-Routine Water
73 Sample-Routine Water
74 Sample-Routine Water
75 Sample-Routine Water
76 Sample-Routine Water
77 Sample-Routine Water
78 Sample-Routine Water
79 Sample-Routine Water
80 Sample-Routine Water
81 Sample-Routine Water
82 Sample-Routine Water
83 Quality Control Sample-Field Replicate Water
84 Sample-Routine Water
85 Sample-Routine Water
86 Sample-Routine Water
87 Sample-Routine Water
88 Sample-Routine Water
89 Sample-Routine Water
90 Sample-Routine Water
91 Sample-Routine Water
92 Sample-Routine Water
93 Sample-Routine Water
94 Sample-Routine Water
95 Sample-Routine Water
96 Sample-Routine Water
97 Sample-Routine Water
98 Sample-Routine Water
99 Sample-Routine Water
100 Sample-Routine Water
101 Sample-Routine Water
102 Sample-Routine Water
103 Sample-Routine Water
104 Sample-Routine Water
105 Sample-Routine Water
106 Sample-Routine Water
107 Sample-Routine Water
108 Sample-Routine Water
109 Sample-Routine Water
110 Sample-Routine Water
111 Sample-Routine Water
112 Sample-Routine Water
113 Sample-Routine Water
114 Sample-Routine Water
115 Sample-Routine Water
116 Sample-Routine Water
117 Sample-Routine Water
118 Sample-Routine Water
119 Sample-Routine Water
120 Sample-Routine Water
121 Sample-Routine Water
122 Sample-Routine Water
123 Sample-Routine Water
124 Sample-Routine Water
125 Sample-Routine Water
126 Quality Control Sample-Field Replicate Water
127 Quality Control Sample-Field Replicate Water
128 Sample-Routine Water
129 Sample-Routine Water
130 Sample-Routine Water
131 Sample-Routine Water
132 Sample-Routine Water
133 Sample-Routine Water
134 Sample-Routine Water
135 Sample-Routine Water
136 Sample-Routine Water
137 Sample-Routine Water
138 Sample-Routine Water
139 Sample-Routine Water
140 Sample-Routine Water
141 Sample-Routine Water
142 Sample-Routine Water
143 Sample-Routine Water
144 Sample-Routine Water
145 Sample-Routine Water
146 Sample-Routine Water
147 Sample-Routine Water
148 Sample-Routine Water
149 Sample-Routine Water
150 Sample-Routine Water
151 Sample-Routine Water
ActivityMediaSubdivisionName ActivityStartDate ActivityStartTime.Time
1 Surface Water 1991-08-19 16:11:00
2 Surface Water 1991-08-19 16:11:00
3 Groundwater 1993-07-27 13:00:00
4 Surface Water 1993-08-24 16:15:00
5 Surface Water 1993-08-24 16:15:00
6 Groundwater 1993-07-21 17:00:00
7 Surface Water 1994-08-24 17:00:00
8 Surface Water 1994-08-24 17:00:00
9 Surface Water 1994-07-19 16:10:00
10 Surface Water 1994-07-19 16:10:00
11 Surface Water 1994-05-20 10:40:00
12 Surface Water 1994-05-20 10:40:00
13 Surface Water 2012-07-18 10:00:00
14 Surface Water 2012-05-17 10:00:00
15 Surface Water 1994-05-04 12:10:00
16 Surface Water 1994-05-04 12:10:00
17 Surface Water 1994-05-19 09:50:00
18 Surface Water 1994-05-19 09:50:00
19 Surface Water 1994-08-25 17:40:00
20 Surface Water 1994-08-25 17:40:00
21 Surface Water 1994-08-24 13:40:00
22 Surface Water 1994-08-24 13:40:00
23 Surface Water 1994-05-18 13:50:00
24 Surface Water 1994-05-18 13:50:00
25 Surface Water 1994-08-24 18:20:00
26 Surface Water 1994-08-24 18:20:00
27 Surface Water 1994-08-01 11:30:00
28 Surface Water 1994-08-01 11:30:00
29 Surface Water 1994-08-25 09:00:00
30 Surface Water 1994-08-25 09:00:00
31 Surface Water 1994-08-24 15:30:00
32 Surface Water 1994-08-24 15:30:00
33 Surface Water 1994-08-24 18:30:00
34 Surface Water 1994-08-24 18:30:00
35 Surface Water 1994-08-25 15:30:00
36 Surface Water 1994-08-25 15:30:00
37 Surface Water 1996-04-18 16:00:00
38 Surface Water 1996-04-18 11:00:00
39 Surface Water 2007-06-27 16:15:00
40 Surface Water 2007-06-27 16:15:00
41 Surface Water 2009-09-09 15:50:00
42 Surface Water 2012-05-17 10:00:00
43 Surface Water 2012-06-12 14:00:00
44 Surface Water 2012-06-12 14:00:00
45 Surface Water 1998-04-22 10:45:00
46 Surface Water 1998-04-22 10:45:00
47 Surface Water 2012-10-09 11:00:00
48 Surface Water 2012-10-09 16:00:00
49 Surface Water 2012-10-09 16:00:00
50 Surface Water 2012-09-14 11:00:00
51 Surface Water 1998-04-22 16:28:00
52 Surface Water 1998-04-22 16:28:00
53 Surface Water 1998-04-22 14:20:00
54 Surface Water 1998-04-22 15:08:00
55 Surface Water 1998-04-22 15:08:00
56 Surface Water 2005-05-11 14:37:00
57 Surface Water 2005-05-05 13:09:00
58 Surface Water 2015-05-27 15:50:00
59 Surface Water 1998-04-22 12:27:00
60 Surface Water 1998-04-22 12:27:00
61 Surface Water 2004-05-11 11:20:00
62 Surface Water 2004-05-07 13:12:00
63 Surface Water 2004-05-07 11:00:00
64 Surface Water 2004-08-19 12:10:00
65 Surface Water 1998-04-22 14:20:00
66 Surface Water 2012-07-18 10:00:00
67 Surface Water 2004-05-07 12:55:00
68 Surface Water 2005-05-04 13:52:00
69 Surface Water 2005-05-17 15:00:00
70 Surface Water 2015-06-10 14:20:00
71 Surface Water 2005-06-01 11:39:00
72 Surface Water 2005-05-16 13:00:00
73 Surface Water 1998-04-22 10:26:00
74 Surface Water 1998-04-22 10:26:00
75 Surface Water 2006-04-27 16:12:00
76 Surface Water 2006-04-26 15:30:00
77 Surface Water 2006-05-30 12:41:00
78 Surface Water 2006-05-03 16:24:00
79 Surface Water 2004-05-07 14:40:00
80 Surface Water 2015-05-27 13:50:00
81 Surface Water 2015-06-17 12:30:00
82 Surface Water 2015-05-13 17:40:00
83 Surface Water 2015-04-22 17:20:00
84 Surface Water 2015-06-03 13:00:00
85 Surface Water 2015-06-10 12:00:00
86 Surface Water 2012-10-09 11:00:00
87 Surface Water 2004-06-01 12:54:00
88 Surface Water 2015-06-17 14:20:00
89 Surface Water 2015-04-15 16:50:00
90 Surface Water 2015-05-20 15:40:00
91 Surface Water 2012-09-14 11:00:00
92 Surface Water 2006-05-18 15:19:00
93 Surface Water 2006-05-31 16:10:00
94 Surface Water 2015-06-03 10:20:00
95 Surface Water 2004-05-07 13:50:00
96 Surface Water 2015-05-06 16:40:00
97 Surface Water 2015-04-29 15:20:00
98 Surface Water 1998-03-24 10:40:00
99 Surface Water 1998-11-09 14:28:00
100 Surface Water 1994-11-01 17:20:00
101 Surface Water 1994-11-01 17:20:00
102 Surface Water 1998-11-09 10:08:00
103 Surface Water 1998-02-11 12:55:00
104 Surface Water 1998-11-09 14:28:00
105 Surface Water 1998-02-11 14:00:00
106 Surface Water 1998-02-11 12:55:00
107 Surface Water 1998-03-24 10:40:00
108 Surface Water 1998-03-24 15:15:00
109 Surface Water 1998-03-04 13:02:00
110 Surface Water 1998-03-04 13:02:00
111 Surface Water 1998-03-04 17:15:00
112 Surface Water 1998-11-09 16:20:00
113 Surface Water 1998-02-11 14:00:00
114 Surface Water 1998-03-04 17:15:00
115 Surface Water 1998-11-09 12:30:00
116 Surface Water 1998-11-09 12:30:00
117 Surface Water 1998-03-24 09:07:00
118 Surface Water 1998-03-24 09:07:00
119 Surface Water 1998-03-24 09:00:00
120 Surface Water 1998-03-24 09:00:00
121 Surface Water 1998-11-09 13:44:00
122 Surface Water 1998-11-09 13:44:00
123 Surface Water 2011-12-29 13:00:00
124 Surface Water 2011-12-29 13:00:00
125 Surface Water 2011-12-29 13:00:00
126 Surface Water 1998-02-10 16:00:00
127 Surface Water 1998-02-10 16:00:00
128 Surface Water 1998-02-10 10:15:00
129 Surface Water 1998-03-04 14:17:00
130 Surface Water 1998-03-04 14:17:00
131 Surface Water 1998-02-11 11:00:00
132 Surface Water 1998-03-04 16:15:00
133 Surface Water 1998-03-04 16:15:00
134 Surface Water 1998-03-24 15:15:00
135 Surface Water 2011-12-29 13:00:00
136 Surface Water 1998-03-04 10:02:00
137 Surface Water 1998-03-24 14:22:00
138 Surface Water 1998-03-24 14:22:00
139 Surface Water 1998-11-09 10:08:00
140 Surface Water 1998-02-10 10:15:00
141 Surface Water 1998-03-24 12:03:00
142 Surface Water 1998-03-24 12:03:00
143 Surface Water 1998-03-04 18:12:00
144 Surface Water 1998-03-04 18:12:00
145 Surface Water 1998-02-11 11:00:00
146 Surface Water 1998-02-10 12:15:00
147 Surface Water 1998-02-10 12:15:00
148 Surface Water 1998-03-04 10:02:00
149 Surface Water 1998-11-09 15:26:00
150 Surface Water 1998-11-09 15:26:00
151 Surface Water 1998-11-09 16:20:00
ActivityStartTime.TimeZoneCode ActivityEndDate ActivityEndTime.Time
1 PDT <NA> <NA>
2 PDT <NA> <NA>
3 PDT <NA> <NA>
4 PDT <NA> <NA>
5 PDT <NA> <NA>
6 PDT <NA> <NA>
7 PDT <NA> <NA>
8 PDT <NA> <NA>
9 PDT <NA> <NA>
10 PDT <NA> <NA>
11 PDT <NA> <NA>
12 PDT <NA> <NA>
13 PDT <NA> <NA>
14 PDT <NA> <NA>
15 PDT <NA> <NA>
16 PDT <NA> <NA>
17 PDT <NA> <NA>
18 PDT <NA> <NA>
19 PDT <NA> <NA>
20 PDT <NA> <NA>
21 PDT <NA> <NA>
22 PDT <NA> <NA>
23 PDT <NA> <NA>
24 PDT <NA> <NA>
25 PDT <NA> <NA>
26 PDT <NA> <NA>
27 PDT <NA> <NA>
28 PDT <NA> <NA>
29 PDT <NA> <NA>
30 PDT <NA> <NA>
31 PDT <NA> <NA>
32 PDT <NA> <NA>
33 PDT <NA> <NA>
34 PDT <NA> <NA>
35 PDT <NA> <NA>
36 PDT <NA> <NA>
37 PDT <NA> <NA>
38 PDT <NA> <NA>
39 PDT <NA> <NA>
40 PDT <NA> <NA>
41 PDT <NA> <NA>
42 PDT <NA> <NA>
43 PDT <NA> <NA>
44 PDT <NA> <NA>
45 PDT <NA> <NA>
46 PDT <NA> <NA>
47 PDT <NA> <NA>
48 PDT <NA> <NA>
49 PDT <NA> <NA>
50 PDT <NA> <NA>
51 PDT <NA> <NA>
52 PDT <NA> <NA>
53 PDT <NA> <NA>
54 PDT <NA> <NA>
55 PDT <NA> <NA>
56 PDT <NA> <NA>
57 PDT <NA> <NA>
58 PDT <NA> <NA>
59 PDT <NA> <NA>
60 PDT <NA> <NA>
61 PDT <NA> <NA>
62 PDT <NA> <NA>
63 PDT <NA> <NA>
64 PDT <NA> <NA>
65 PDT <NA> <NA>
66 PDT <NA> <NA>
67 PDT <NA> <NA>
68 PDT <NA> <NA>
69 PDT <NA> <NA>
70 PDT <NA> <NA>
71 PDT <NA> <NA>
72 PDT <NA> <NA>
73 PDT <NA> <NA>
74 PDT <NA> <NA>
75 PDT <NA> <NA>
76 PDT <NA> <NA>
77 PDT <NA> <NA>
78 PDT <NA> <NA>
79 PDT <NA> <NA>
80 PDT <NA> <NA>
81 PDT <NA> <NA>
82 PDT <NA> <NA>
83 PDT <NA> <NA>
84 PDT <NA> <NA>
85 PDT <NA> <NA>
86 PDT <NA> <NA>
87 PDT <NA> <NA>
88 PDT <NA> <NA>
89 PDT <NA> <NA>
90 PDT <NA> <NA>
91 PDT <NA> <NA>
92 PDT <NA> <NA>
93 PDT <NA> <NA>
94 PDT <NA> <NA>
95 PDT <NA> <NA>
96 PDT <NA> <NA>
97 PDT <NA> <NA>
98 PST <NA> <NA>
99 PST <NA> <NA>
100 PST <NA> <NA>
101 PST <NA> <NA>
102 PST <NA> <NA>
103 PST <NA> <NA>
104 PST <NA> <NA>
105 PST <NA> <NA>
106 PST <NA> <NA>
107 PST <NA> <NA>
108 PST <NA> <NA>
109 PST <NA> <NA>
110 PST <NA> <NA>
111 PST <NA> <NA>
112 PST <NA> <NA>
113 PST <NA> <NA>
114 PST <NA> <NA>
115 PST <NA> <NA>
116 PST <NA> <NA>
117 PST <NA> <NA>
118 PST <NA> <NA>
119 PST <NA> <NA>
120 PST <NA> <NA>
121 PST <NA> <NA>
122 PST <NA> <NA>
123 PST <NA> <NA>
124 PST <NA> <NA>
125 PST <NA> <NA>
126 PST <NA> <NA>
127 PST <NA> <NA>
128 PST <NA> <NA>
129 PST <NA> <NA>
130 PST <NA> <NA>
131 PST <NA> <NA>
132 PST <NA> <NA>
133 PST <NA> <NA>
134 PST <NA> <NA>
135 PST <NA> <NA>
136 PST <NA> <NA>
137 PST <NA> <NA>
138 PST <NA> <NA>
139 PST <NA> <NA>
140 PST <NA> <NA>
141 PST <NA> <NA>
142 PST <NA> <NA>
143 PST <NA> <NA>
144 PST <NA> <NA>
145 PST <NA> <NA>
146 PST <NA> <NA>
147 PST <NA> <NA>
148 PST <NA> <NA>
149 PST <NA> <NA>
150 PST <NA> <NA>
151 PST <NA> <NA>
ActivityEndTime.TimeZoneCode ActivityRelativeDepthName
1 <NA> <NA>
2 <NA> <NA>
3 <NA> <NA>
4 <NA> <NA>
5 <NA> <NA>
6 <NA> <NA>
7 <NA> <NA>
8 <NA> <NA>
9 <NA> <NA>
10 <NA> <NA>
11 <NA> <NA>
12 <NA> <NA>
13 <NA> <NA>
14 <NA> <NA>
15 <NA> <NA>
16 <NA> <NA>
17 <NA> <NA>
18 <NA> <NA>
19 <NA> <NA>
20 <NA> <NA>
21 <NA> <NA>
22 <NA> <NA>
23 <NA> <NA>
24 <NA> <NA>
25 <NA> <NA>
26 <NA> <NA>
27 <NA> <NA>
28 <NA> <NA>
29 <NA> <NA>
30 <NA> <NA>
31 <NA> <NA>
32 <NA> <NA>
33 <NA> <NA>
34 <NA> <NA>
35 <NA> <NA>
36 <NA> <NA>
37 <NA> <NA>
38 <NA> <NA>
39 <NA> <NA>
40 <NA> <NA>
41 <NA> <NA>
42 <NA> <NA>
43 <NA> <NA>
44 <NA> <NA>
45 <NA> <NA>
46 <NA> <NA>
47 <NA> <NA>
48 <NA> <NA>
49 <NA> <NA>
50 <NA> <NA>
51 <NA> <NA>
52 <NA> <NA>
53 <NA> <NA>
54 <NA> <NA>
55 <NA> <NA>
56 <NA> <NA>
57 <NA> <NA>
58 <NA> <NA>
59 <NA> <NA>
60 <NA> <NA>
61 <NA> <NA>
62 <NA> <NA>
63 <NA> <NA>
64 <NA> <NA>
65 <NA> <NA>
66 <NA> <NA>
67 <NA> <NA>
68 <NA> <NA>
69 <NA> <NA>
70 <NA> <NA>
71 <NA> <NA>
72 <NA> <NA>
73 <NA> <NA>
74 <NA> <NA>
75 <NA> <NA>
76 <NA> <NA>
77 <NA> <NA>
78 <NA> <NA>
79 <NA> <NA>
80 <NA> <NA>
81 <NA> <NA>
82 <NA> <NA>
83 <NA> <NA>
84 <NA> <NA>
85 <NA> <NA>
86 <NA> <NA>
87 <NA> <NA>
88 <NA> <NA>
89 <NA> <NA>
90 <NA> <NA>
91 <NA> <NA>
92 <NA> <NA>
93 <NA> <NA>
94 <NA> <NA>
95 <NA> <NA>
96 <NA> <NA>
97 <NA> <NA>
98 <NA> <NA>
99 <NA> <NA>
100 <NA> <NA>
101 <NA> <NA>
102 <NA> <NA>
103 <NA> <NA>
104 <NA> <NA>
105 <NA> <NA>
106 <NA> <NA>
107 <NA> <NA>
108 <NA> <NA>
109 <NA> <NA>
110 <NA> <NA>
111 <NA> <NA>
112 <NA> <NA>
113 <NA> <NA>
114 <NA> <NA>
115 <NA> <NA>
116 <NA> <NA>
117 <NA> <NA>
118 <NA> <NA>
119 <NA> <NA>
120 <NA> <NA>
121 <NA> <NA>
122 <NA> <NA>
123 <NA> <NA>
124 <NA> <NA>
125 <NA> <NA>
126 <NA> <NA>
127 <NA> <NA>
128 <NA> <NA>
129 <NA> <NA>
130 <NA> <NA>
131 <NA> <NA>
132 <NA> <NA>
133 <NA> <NA>
134 <NA> <NA>
135 <NA> <NA>
136 <NA> <NA>
137 <NA> <NA>
138 <NA> <NA>
139 <NA> <NA>
140 <NA> <NA>
141 <NA> <NA>
142 <NA> <NA>
143 <NA> <NA>
144 <NA> <NA>
145 <NA> <NA>
146 <NA> <NA>
147 <NA> <NA>
148 <NA> <NA>
149 <NA> <NA>
150 <NA> <NA>
151 <NA> <NA>
ActivityDepthHeightMeasure.MeasureValue
1 NA
2 NA
3 NA
4 NA
5 NA
6 NA
7 NA
8 NA
9 NA
10 NA
11 NA
12 NA
13 NA
14 NA
15 NA
16 NA
17 NA
18 NA
19 NA
20 NA
21 NA
22 NA
23 NA
24 NA
25 NA
26 NA
27 NA
28 NA
29 NA
30 NA
31 NA
32 NA
33 NA
34 NA
35 NA
36 NA
37 NA
38 NA
39 NA
40 NA
41 NA
42 NA
43 NA
44 NA
45 NA
46 NA
47 NA
48 NA
49 NA
50 NA
51 NA
52 NA
53 NA
54 NA
55 NA
56 NA
57 NA
58 NA
59 NA
60 NA
61 NA
62 NA
63 NA
64 NA
65 NA
66 NA
67 NA
68 NA
69 NA
70 NA
71 NA
72 NA
73 NA
74 NA
75 NA
76 NA
77 NA
78 NA
79 NA
80 NA
81 NA
82 NA
83 NA
84 NA
85 NA
86 NA
87 NA
88 NA
89 NA
90 NA
91 NA
92 NA
93 NA
94 NA
95 NA
96 NA
97 NA
98 NA
99 NA
100 NA
101 NA
102 NA
103 NA
104 NA
105 NA
106 NA
107 NA
108 NA
109 NA
110 NA
111 NA
112 NA
113 NA
114 NA
115 NA
116 NA
117 NA
118 NA
119 NA
120 NA
121 NA
122 NA
123 NA
124 NA
125 NA
126 NA
127 NA
128 NA
129 NA
130 NA
131 NA
132 NA
133 NA
134 NA
135 NA
136 NA
137 NA
138 NA
139 NA
140 NA
141 NA
142 NA
143 NA
144 NA
145 NA
146 NA
147 NA
148 NA
149 NA
150 NA
151 NA
ActivityDepthHeightMeasure.MeasureUnitCode
1 <NA>
2 <NA>
3 <NA>
4 <NA>
5 <NA>
6 <NA>
7 <NA>
8 <NA>
9 <NA>
10 <NA>
11 <NA>
12 <NA>
13 <NA>
14 <NA>
15 <NA>
16 <NA>
17 <NA>
18 <NA>
19 <NA>
20 <NA>
21 <NA>
22 <NA>
23 <NA>
24 <NA>
25 <NA>
26 <NA>
27 <NA>
28 <NA>
29 <NA>
30 <NA>
31 <NA>
32 <NA>
33 <NA>
34 <NA>
35 <NA>
36 <NA>
37 <NA>
38 <NA>
39 <NA>
40 <NA>
41 <NA>
42 <NA>
43 <NA>
44 <NA>
45 <NA>
46 <NA>
47 <NA>
48 <NA>
49 <NA>
50 <NA>
51 <NA>
52 <NA>
53 <NA>
54 <NA>
55 <NA>
56 <NA>
57 <NA>
58 <NA>
59 <NA>
60 <NA>
61 <NA>
62 <NA>
63 <NA>
64 <NA>
65 <NA>
66 <NA>
67 <NA>
68 <NA>
69 <NA>
70 <NA>
71 <NA>
72 <NA>
73 <NA>
74 <NA>
75 <NA>
76 <NA>
77 <NA>
78 <NA>
79 <NA>
80 <NA>
81 <NA>
82 <NA>
83 <NA>
84 <NA>
85 <NA>
86 <NA>
87 <NA>
88 <NA>
89 <NA>
90 <NA>
91 <NA>
92 <NA>
93 <NA>
94 <NA>
95 <NA>
96 <NA>
97 <NA>
98 <NA>
99 <NA>
100 <NA>
101 <NA>
102 <NA>
103 <NA>
104 <NA>
105 <NA>
106 <NA>
107 <NA>
108 <NA>
109 <NA>
110 <NA>
111 <NA>
112 <NA>
113 <NA>
114 <NA>
115 <NA>
116 <NA>
117 <NA>
118 <NA>
119 <NA>
120 <NA>
121 <NA>
122 <NA>
123 <NA>
124 <NA>
125 <NA>
126 <NA>
127 <NA>
128 <NA>
129 <NA>
130 <NA>
131 <NA>
132 <NA>
133 <NA>
134 <NA>
135 <NA>
136 <NA>
137 <NA>
138 <NA>
139 <NA>
140 <NA>
141 <NA>
142 <NA>
143 <NA>
144 <NA>
145 <NA>
146 <NA>
147 <NA>
148 <NA>
149 <NA>
150 <NA>
151 <NA>
ActivityDepthAltitudeReferencePointText
1 <NA>
2 <NA>
3 <NA>
4 <NA>
5 <NA>
6 <NA>
7 <NA>
8 <NA>
9 <NA>
10 <NA>
11 <NA>
12 <NA>
13 <NA>
14 <NA>
15 <NA>
16 <NA>
17 <NA>
18 <NA>
19 <NA>
20 <NA>
21 <NA>
22 <NA>
23 <NA>
24 <NA>
25 <NA>
26 <NA>
27 <NA>
28 <NA>
29 <NA>
30 <NA>
31 <NA>
32 <NA>
33 <NA>
34 <NA>
35 <NA>
36 <NA>
37 <NA>
38 <NA>
39 <NA>
40 <NA>
41 <NA>
42 <NA>
43 <NA>
44 <NA>
45 <NA>
46 <NA>
47 <NA>
48 <NA>
49 <NA>
50 <NA>
51 <NA>
52 <NA>
53 <NA>
54 <NA>
55 <NA>
56 <NA>
57 <NA>
58 <NA>
59 <NA>
60 <NA>
61 <NA>
62 <NA>
63 <NA>
64 <NA>
65 <NA>
66 <NA>
67 <NA>
68 <NA>
69 <NA>
70 <NA>
71 <NA>
72 <NA>
73 <NA>
74 <NA>
75 <NA>
76 <NA>
77 <NA>
78 <NA>
79 <NA>
80 <NA>
81 <NA>
82 <NA>
83 <NA>
84 <NA>
85 <NA>
86 <NA>
87 <NA>
88 <NA>
89 <NA>
90 <NA>
91 <NA>
92 <NA>
93 <NA>
94 <NA>
95 <NA>
96 <NA>
97 <NA>
98 <NA>
99 <NA>
100 <NA>
101 <NA>
102 <NA>
103 <NA>
104 <NA>
105 <NA>
106 <NA>
107 <NA>
108 <NA>
109 <NA>
110 <NA>
111 <NA>
112 <NA>
113 <NA>
114 <NA>
115 <NA>
116 <NA>
117 <NA>
118 <NA>
119 <NA>
120 <NA>
121 <NA>
122 <NA>
123 <NA>
124 <NA>
125 <NA>
126 <NA>
127 <NA>
128 <NA>
129 <NA>
130 <NA>
131 <NA>
132 <NA>
133 <NA>
134 <NA>
135 <NA>
136 <NA>
137 <NA>
138 <NA>
139 <NA>
140 <NA>
141 <NA>
142 <NA>
143 <NA>
144 <NA>
145 <NA>
146 <NA>
147 <NA>
148 <NA>
149 <NA>
150 <NA>
151 <NA>
ActivityTopDepthHeightMeasure.MeasureValue
1 NA
2 NA
3 NA
4 NA
5 NA
6 NA
7 NA
8 NA
9 NA
10 NA
11 NA
12 NA
13 NA
14 NA
15 NA
16 NA
17 NA
18 NA
19 NA
20 NA
21 NA
22 NA
23 NA
24 NA
25 NA
26 NA
27 NA
28 NA
29 NA
30 NA
31 NA
32 NA
33 NA
34 NA
35 NA
36 NA
37 NA
38 NA
39 NA
40 NA
41 NA
42 NA
43 NA
44 NA
45 NA
46 NA
47 NA
48 NA
49 NA
50 NA
51 NA
52 NA
53 NA
54 NA
55 NA
56 NA
57 NA
58 NA
59 NA
60 NA
61 NA
62 NA
63 NA
64 NA
65 NA
66 NA
67 NA
68 NA
69 NA
70 NA
71 NA
72 NA
73 NA
74 NA
75 NA
76 NA
77 NA
78 NA
79 NA
80 NA
81 NA
82 NA
83 NA
84 NA
85 NA
86 NA
87 NA
88 NA
89 NA
90 NA
91 NA
92 NA
93 NA
94 NA
95 NA
96 NA
97 NA
98 NA
99 NA
100 NA
101 NA
102 NA
103 NA
104 NA
105 NA
106 NA
107 NA
108 NA
109 NA
110 NA
111 NA
112 NA
113 NA
114 NA
115 NA
116 NA
117 NA
118 NA
119 NA
120 NA
121 NA
122 NA
123 NA
124 NA
125 NA
126 NA
127 NA
128 NA
129 NA
130 NA
131 NA
132 NA
133 NA
134 NA
135 NA
136 NA
137 NA
138 NA
139 NA
140 NA
141 NA
142 NA
143 NA
144 NA
145 NA
146 NA
147 NA
148 NA
149 NA
150 NA
151 NA
ActivityTopDepthHeightMeasure.MeasureUnitCode
1 <NA>
2 <NA>
3 <NA>
4 <NA>
5 <NA>
6 <NA>
7 <NA>
8 <NA>
9 <NA>
10 <NA>
11 <NA>
12 <NA>
13 <NA>
14 <NA>
15 <NA>
16 <NA>
17 <NA>
18 <NA>
19 <NA>
20 <NA>
21 <NA>
22 <NA>
23 <NA>
24 <NA>
25 <NA>
26 <NA>
27 <NA>
28 <NA>
29 <NA>
30 <NA>
31 <NA>
32 <NA>
33 <NA>
34 <NA>
35 <NA>
36 <NA>
37 <NA>
38 <NA>
39 <NA>
40 <NA>
41 <NA>
42 <NA>
43 <NA>
44 <NA>
45 <NA>
46 <NA>
47 <NA>
48 <NA>
49 <NA>
50 <NA>
51 <NA>
52 <NA>
53 <NA>
54 <NA>
55 <NA>
56 <NA>
57 <NA>
58 <NA>
59 <NA>
60 <NA>
61 <NA>
62 <NA>
63 <NA>
64 <NA>
65 <NA>
66 <NA>
67 <NA>
68 <NA>
69 <NA>
70 <NA>
71 <NA>
72 <NA>
73 <NA>
74 <NA>
75 <NA>
76 <NA>
77 <NA>
78 <NA>
79 <NA>
80 <NA>
81 <NA>
82 <NA>
83 <NA>
84 <NA>
85 <NA>
86 <NA>
87 <NA>
88 <NA>
89 <NA>
90 <NA>
91 <NA>
92 <NA>
93 <NA>
94 <NA>
95 <NA>
96 <NA>
97 <NA>
98 <NA>
99 <NA>
100 <NA>
101 <NA>
102 <NA>
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87 <NA> <NA>
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103 <NA> <NA>
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108 <NA> <NA>
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110 <NA> <NA>
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ActivityCommentText
1 <NA>
2 <NA>
3 <NA>
4 A-2420273 Default ASR Comment 50MLS FILTERED FOR SOC
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6 <NA>
7 <NA>
8 <NA>
9 <NA>
10 <NA>
11 <NA>
12 <NA>
13 L-2050114 low volume FCC, paa Received July 21, 2012
14 L-1640042 X = No time on bottles X = Please do not send your empty acid vials in the cooler - dispose of them on your end.. corrected ASR year to 2012, paa, 6/15/12
15 <NA>
16 <NA>
17 <NA>
18 <NA>
19 <NA>
20 <NA>
21 <NA>
22 <NA>
23 <NA>
24 <NA>
25 <NA>
26 <NA>
27 <NA>
28 <NA>
29 <NA>
30 <NA>
31 <NA>
32 <NA>
33 <NA>
34 <NA>
35 <NA>
36 <NA>
37 <NA>
38 <NA>
39 <NA>
40 <NA>
41 MM-18698A
42 L-1640042 X = No time on bottles X = Please do not send your empty acid vials in the cooler - dispose of them on your end.. corrected ASR year to 2012, paa, 6/15/12
43 L-1700101 X = Date on bottles reads 6/6/2012 Received June 16, 2012
44 L-1700101 X = Date on bottles reads 6/6/2012 Received June 16, 2012
45 <NA>
46 <NA>
47 <NA>
48 <NA>
49 <NA>
50 <NA>
51 <NA>
52 <NA>
53 <NA>
54 <NA>
55 <NA>
56 A-1360034 Water collected coincidental with trappingL-1360034 Received May 13,2005
57 A-1300095 Water collected coincidental with trapsL-1300095 X= Day, Month & Time taken from samples
58 <NA>
59 <NA>
60 <NA>
61 A-1340157 Vol. filtered TPCN= 200.4 mL (sample A),Vol. Filtered TPCN= 209.3 mL (sample B)L-1340157 Cooler recvd late (11:30 am 5/13/04) according to DOI contract with FedEX. File claim with FedEX.com for late delivery charges.
62 <NA>
63 <NA>
64 A-2360210 volume filtered for tpcn= 228.9 ml (sample a) volume filtered for tpcn= 232.2 ml (sample b)
65 <NA>
66 L-2050114 low volume FCC, paa Received July 21, 2012
67 <NA>
68 A-1300094 Water collected coincidental with traps
69 <NA>
70 <NA>
71 <NA>
72 <NA>
73 <NA>
74 <NA>
75 A-1210008 Composite sample collected at trap locations. L-1210008 Received April 29, 2006
76 A-1210009 Composite sample collected at trap locations. L-1210009 Received April 29,2006
77 A-1530121 Composite sample collected at trap locations.
78 <NA>
79 <NA>
80 <NA>
81 <NA>
82 <NA>
83 L-1190018 FED EX LATE DELIVERY
84 <NA>
85 <NA>
86 <NA>
87 L-1550194 x=improper sample container= wca
88 MM-41807B
89 L-1120013 FED EX LATE DELIVERY. Updated Time from 1640 per J. Morace. JPC 7/18/18
90 <NA>
91 <NA>
92 A-1420004 Composite sample collected at map locations. L-1420004 Received May 20,2006
93 A-1530122 Composite sample collected at trap locations.
94 <NA>
95 A-1320032 heavy cow use, cows present
96 <NA>
97 <NA>
98 <NA>
99 <NA>
100 <NA>
101 <NA>
102 <NA>
103 <NA>
104 <NA>
105 <NA>
106 <NA>
107 <NA>
108 <NA>
109 <NA>
110 <NA>
111 <NA>
112 <NA>
113 <NA>
114 <NA>
115 <NA>
116 <NA>
117 <NA>
118 <NA>
119 <NA>
120 <NA>
121 <NA>
122 <NA>
123 L-0090036 CHY recvd warm with routine smpls, ok log per Tara, paa, 1/9/12 Frozen containers received warm at 5.8 Degrees C
124 L-0090035 CHY recvd warm with routine smpls, ok log per Tara, paa, 1/9/12 Received December 30, 2011 Frozen containers received warm at 5.8 Degrees C
125 L-0090035 CHY recvd warm with routine smpls, ok log per Tara, paa, 1/9/12 Received December 30, 2011 Frozen containers received warm at 5.8 Degrees C
126 <NA>
127 <NA>
128 <NA>
129 <NA>
130 <NA>
131 <NA>
132 <NA>
133 <NA>
134 <NA>
135 L-0090036 CHY recvd warm with routine smpls, ok log per Tara, paa, 1/9/12 Frozen containers received warm at 5.8 Degrees C
136 <NA>
137 <NA>
138 <NA>
139 <NA>
140 <NA>
141 <NA>
142 <NA>
143 <NA>
144 <NA>
145 <NA>
146 <NA>
147 <NA>
148 <NA>
149 <NA>
150 <NA>
151 <NA>
SampleAquifer HydrologicCondition HydrologicEvent
1 <NA> Stable, normal stage Routine sample
2 <NA> Stable, normal stage Routine sample
3 <NA> Not determined Routine sample
4 <NA> Stable, low stage Routine sample
5 <NA> Stable, low stage Routine sample
6 <NA> Not determined Routine sample
7 <NA> Not determined Routine sample
8 <NA> Not determined Routine sample
9 <NA> Stable, normal stage Routine sample
10 <NA> Stable, normal stage Routine sample
11 <NA> Stable, normal stage Routine sample
12 <NA> Stable, normal stage Routine sample
13 <NA> Stable, normal stage Routine sample
14 <NA> Stable, normal stage Routine sample
15 <NA> Stable, high stage Routine sample
16 <NA> Stable, high stage Routine sample
17 <NA> Stable, normal stage Routine sample
18 <NA> Stable, normal stage Routine sample
19 <NA> Not determined Routine sample
20 <NA> Not determined Routine sample
21 <NA> Not determined Routine sample
22 <NA> Not determined Routine sample
23 <NA> Stable, normal stage Routine sample
24 <NA> Stable, normal stage Routine sample
25 <NA> Not determined Routine sample
26 <NA> Not determined Routine sample
27 <NA> Stable, normal stage Routine sample
28 <NA> Stable, normal stage Routine sample
29 <NA> Not determined Routine sample
30 <NA> Not determined Routine sample
31 <NA> Stable, normal stage Routine sample
32 <NA> Stable, normal stage Routine sample
33 <NA> Not determined Routine sample
34 <NA> Not determined Routine sample
35 <NA> Not determined Routine sample
36 <NA> Not determined Routine sample
37 <NA> Stable, high stage Storm
38 <NA> Stable, high stage Storm
39 <NA> Not determined Routine sample
40 <NA> Not determined Routine sample
41 <NA> Stable, normal stage Routine sample
42 <NA> Stable, normal stage Routine sample
43 <NA> Stable, normal stage Routine sample
44 <NA> Stable, normal stage Routine sample
45 <NA> Stable, normal stage Routine sample
46 <NA> Stable, normal stage Routine sample
47 <NA> Stable, normal stage Routine sample
48 <NA> Stable, normal stage Routine sample
49 <NA> Stable, normal stage Routine sample
50 <NA> Stable, normal stage Routine sample
51 <NA> Stable, normal stage Routine sample
52 <NA> Stable, normal stage Routine sample
53 <NA> Stable, normal stage Routine sample
54 <NA> Stable, normal stage Routine sample
55 <NA> Stable, normal stage Routine sample
56 <NA> Stable, normal stage Routine sample
57 <NA> Not determined Routine sample
58 <NA> Stable, low stage Routine sample
59 <NA> Stable, normal stage Routine sample
60 <NA> Stable, normal stage Routine sample
61 <NA> Stable, normal stage Routine sample
62 <NA> Not determined Routine sample
63 <NA> Not determined Routine sample
64 <NA> Stable, normal stage Routine sample
65 <NA> Stable, normal stage Routine sample
66 <NA> Stable, normal stage Routine sample
67 <NA> Not determined Routine sample
68 <NA> Not determined Routine sample
69 <NA> Stable, normal stage Routine sample
70 <NA> Stable, normal stage Routine sample
71 <NA> Stable, normal stage Routine sample
72 <NA> Stable, normal stage Routine sample
73 <NA> Stable, normal stage Routine sample
74 <NA> Stable, normal stage Routine sample
75 <NA> Stable, normal stage Routine sample
76 <NA> Stable, normal stage Routine sample
77 <NA> Stable, normal stage Routine sample
78 <NA> Stable, normal stage Routine sample
79 <NA> Not determined Routine sample
80 <NA> Not determined Routine sample
81 <NA> Stable, normal stage Routine sample
82 <NA> Stable, low stage Routine sample
83 <NA> Not determined Routine sample
84 <NA> Stable, normal stage Routine sample
85 <NA> Stable, normal stage Routine sample
86 <NA> Stable, normal stage Routine sample
87 <NA> Not determined Routine sample
88 <NA> Stable, normal stage Routine sample
89 <NA> Stable, normal stage Routine sample
90 <NA> Not determined Routine sample
91 <NA> Stable, normal stage Routine sample
92 <NA> Stable, normal stage Routine sample
93 <NA> Stable, normal stage Routine sample
94 <NA> Stable, normal stage Routine sample
95 <NA> Not determined Routine sample
96 <NA> Stable, low stage Routine sample
97 <NA> Stable, normal stage Routine sample
98 <NA> Stable, normal stage Routine sample
99 <NA> Stable, normal stage Routine sample
100 <NA> Rising stage Storm
101 <NA> Rising stage Storm
102 <NA> Stable, normal stage Routine sample
103 <NA> Stable, normal stage Routine sample
104 <NA> Stable, normal stage Routine sample
105 <NA> Stable, normal stage Routine sample
106 <NA> Stable, normal stage Routine sample
107 <NA> Stable, normal stage Routine sample
108 <NA> Stable, normal stage Routine sample
109 <NA> Stable, normal stage Routine sample
110 <NA> Stable, normal stage Routine sample
111 <NA> Stable, normal stage Routine sample
112 <NA> Stable, normal stage Routine sample
113 <NA> Stable, normal stage Routine sample
114 <NA> Stable, normal stage Routine sample
115 <NA> Stable, normal stage Routine sample
116 <NA> Stable, normal stage Routine sample
117 <NA> Stable, normal stage Routine sample
118 <NA> Stable, normal stage Routine sample
119 <NA> Stable, normal stage Routine sample
120 <NA> Stable, normal stage Routine sample
121 <NA> Stable, normal stage Routine sample
122 <NA> Stable, normal stage Routine sample
123 <NA> Stable, normal stage Routine sample
124 <NA> Stable, normal stage Routine sample
125 <NA> Stable, normal stage Routine sample
126 <NA> Stable, normal stage Routine sample
127 <NA> Stable, normal stage Routine sample
128 <NA> Stable, normal stage Routine sample
129 <NA> Stable, normal stage Routine sample
130 <NA> Stable, normal stage Routine sample
131 <NA> Stable, normal stage Routine sample
132 <NA> Stable, normal stage Routine sample
133 <NA> Stable, normal stage Routine sample
134 <NA> Stable, normal stage Routine sample
135 <NA> Stable, normal stage Routine sample
136 <NA> Stable, normal stage Routine sample
137 <NA> Stable, normal stage Routine sample
138 <NA> Stable, normal stage Routine sample
139 <NA> Stable, normal stage Routine sample
140 <NA> Stable, normal stage Routine sample
141 <NA> Stable, normal stage Routine sample
142 <NA> Stable, normal stage Routine sample
143 <NA> Stable, normal stage Routine sample
144 <NA> Stable, normal stage Routine sample
145 <NA> Stable, normal stage Routine sample
146 <NA> Stable, normal stage Routine sample
147 <NA> Stable, normal stage Routine sample
148 <NA> Stable, normal stage Routine sample
149 <NA> Stable, normal stage Routine sample
150 <NA> Stable, normal stage Routine sample
151 <NA> Stable, normal stage Routine sample
ActivityLocation.LatitudeMeasure ActivityLocation.LongitudeMeasure
1 <NA> <NA>
2 <NA> <NA>
3 <NA> <NA>
4 <NA> <NA>
5 <NA> <NA>
6 <NA> <NA>
7 <NA> <NA>
8 <NA> <NA>
9 <NA> <NA>
10 <NA> <NA>
11 <NA> <NA>
12 <NA> <NA>
13 <NA> <NA>
14 <NA> <NA>
15 <NA> <NA>
16 <NA> <NA>
17 <NA> <NA>
18 <NA> <NA>
19 <NA> <NA>
20 <NA> <NA>
21 <NA> <NA>
22 <NA> <NA>
23 <NA> <NA>
24 <NA> <NA>
25 <NA> <NA>
26 <NA> <NA>
27 <NA> <NA>
28 <NA> <NA>
29 <NA> <NA>
30 <NA> <NA>
31 <NA> <NA>
32 <NA> <NA>
33 <NA> <NA>
34 <NA> <NA>
35 <NA> <NA>
36 <NA> <NA>
37 <NA> <NA>
38 <NA> <NA>
39 <NA> <NA>
40 <NA> <NA>
41 <NA> <NA>
42 <NA> <NA>
43 <NA> <NA>
44 <NA> <NA>
45 <NA> <NA>
46 <NA> <NA>
47 <NA> <NA>
48 <NA> <NA>
49 <NA> <NA>
50 <NA> <NA>
51 <NA> <NA>
52 <NA> <NA>
53 <NA> <NA>
54 <NA> <NA>
55 <NA> <NA>
56 <NA> <NA>
57 <NA> <NA>
58 <NA> <NA>
59 <NA> <NA>
60 <NA> <NA>
61 <NA> <NA>
62 <NA> <NA>
63 <NA> <NA>
64 <NA> <NA>
65 <NA> <NA>
66 <NA> <NA>
67 <NA> <NA>
68 <NA> <NA>
69 <NA> <NA>
70 <NA> <NA>
71 <NA> <NA>
72 <NA> <NA>
73 <NA> <NA>
74 <NA> <NA>
75 <NA> <NA>
76 <NA> <NA>
77 <NA> <NA>
78 <NA> <NA>
79 <NA> <NA>
80 <NA> <NA>
81 <NA> <NA>
82 <NA> <NA>
83 <NA> <NA>
84 <NA> <NA>
85 <NA> <NA>
86 <NA> <NA>
87 <NA> <NA>
88 <NA> <NA>
89 <NA> <NA>
90 <NA> <NA>
91 <NA> <NA>
92 <NA> <NA>
93 <NA> <NA>
94 <NA> <NA>
95 <NA> <NA>
96 <NA> <NA>
97 <NA> <NA>
98 <NA> <NA>
99 <NA> <NA>
100 <NA> <NA>
101 <NA> <NA>
102 <NA> <NA>
103 <NA> <NA>
104 <NA> <NA>
105 <NA> <NA>
106 <NA> <NA>
107 <NA> <NA>
108 <NA> <NA>
109 <NA> <NA>
110 <NA> <NA>
111 <NA> <NA>
112 <NA> <NA>
113 <NA> <NA>
114 <NA> <NA>
115 <NA> <NA>
116 <NA> <NA>
117 <NA> <NA>
118 <NA> <NA>
119 <NA> <NA>
120 <NA> <NA>
121 <NA> <NA>
122 <NA> <NA>
123 <NA> <NA>
124 <NA> <NA>
125 <NA> <NA>
126 <NA> <NA>
127 <NA> <NA>
128 <NA> <NA>
129 <NA> <NA>
130 <NA> <NA>
131 <NA> <NA>
132 <NA> <NA>
133 <NA> <NA>
134 <NA> <NA>
135 <NA> <NA>
136 <NA> <NA>
137 <NA> <NA>
138 <NA> <NA>
139 <NA> <NA>
140 <NA> <NA>
141 <NA> <NA>
142 <NA> <NA>
143 <NA> <NA>
144 <NA> <NA>
145 <NA> <NA>
146 <NA> <NA>
147 <NA> <NA>
148 <NA> <NA>
149 <NA> <NA>
150 <NA> <NA>
151 <NA> <NA>
SampleCollectionMethod.MethodIdentifier
1 USGS
2 USGS
3 USGS
4 USGS
5 USGS
6 USGS
7 USGS
8 USGS
9 USGS
10 USGS
11 USGS
12 USGS
13 USGS
14 USGS
15 USGS
16 USGS
17 USGS
18 USGS
19 USGS
20 USGS
21 USGS
22 USGS
23 USGS
24 USGS
25 USGS
26 USGS
27 USGS
28 USGS
29 USGS
30 USGS
31 USGS
32 USGS
33 USGS
34 USGS
35 USGS
36 USGS
37 USGS
38 USGS
39 USGS
40 USGS
41 USGS
42 USGS
43 USGS
44 USGS
45 USGS
46 USGS
47 USGS
48 USGS
49 USGS
50 USGS
51 USGS
52 USGS
53 USGS
54 USGS
55 USGS
56 USGS
57 USGS
58 70
59 USGS
60 USGS
61 USGS
62 USGS
63 USGS
64 USGS
65 USGS
66 USGS
67 USGS
68 USGS
69 USGS
70 70
71 USGS
72 USGS
73 USGS
74 USGS
75 USGS
76 USGS
77 USGS
78 USGS
79 USGS
80 40
81 70
82 70
83 70
84 40
85 70
86 USGS
87 USGS
88 70
89 70
90 70
91 USGS
92 USGS
93 USGS
94 70
95 USGS
96 70
97 70
98 USGS
99 USGS
100 USGS
101 USGS
102 USGS
103 USGS
104 USGS
105 USGS
106 USGS
107 USGS
108 USGS
109 USGS
110 USGS
111 USGS
112 USGS
113 USGS
114 USGS
115 USGS
116 USGS
117 USGS
118 USGS
119 USGS
120 USGS
121 USGS
122 USGS
123 USGS
124 USGS
125 USGS
126 USGS
127 USGS
128 USGS
129 USGS
130 USGS
131 USGS
132 USGS
133 USGS
134 USGS
135 USGS
136 USGS
137 USGS
138 USGS
139 USGS
140 USGS
141 USGS
142 USGS
143 USGS
144 USGS
145 USGS
146 USGS
147 USGS
148 USGS
149 USGS
150 USGS
151 USGS
SampleCollectionMethod.MethodIdentifierContext
1 USGS
2 USGS
3 USGS
4 USGS
5 USGS
6 USGS
7 USGS
8 USGS
9 USGS
10 USGS
11 USGS
12 USGS
13 USGS
14 USGS
15 USGS
16 USGS
17 USGS
18 USGS
19 USGS
20 USGS
21 USGS
22 USGS
23 USGS
24 USGS
25 USGS
26 USGS
27 USGS
28 USGS
29 USGS
30 USGS
31 USGS
32 USGS
33 USGS
34 USGS
35 USGS
36 USGS
37 USGS
38 USGS
39 USGS
40 USGS
41 USGS
42 USGS
43 USGS
44 USGS
45 USGS
46 USGS
47 USGS
48 USGS
49 USGS
50 USGS
51 USGS
52 USGS
53 USGS
54 USGS
55 USGS
56 USGS
57 USGS
58 USGS parameter code 82398
59 USGS
60 USGS
61 USGS
62 USGS
63 USGS
64 USGS
65 USGS
66 USGS
67 USGS
68 USGS
69 USGS
70 USGS parameter code 82398
71 USGS
72 USGS
73 USGS
74 USGS
75 USGS
76 USGS
77 USGS
78 USGS
79 USGS
80 USGS parameter code 82398
81 USGS parameter code 82398
82 USGS parameter code 82398
83 USGS parameter code 82398
84 USGS parameter code 82398
85 USGS parameter code 82398
86 USGS
87 USGS
88 USGS parameter code 82398
89 USGS parameter code 82398
90 USGS parameter code 82398
91 USGS
92 USGS
93 USGS
94 USGS parameter code 82398
95 USGS
96 USGS parameter code 82398
97 USGS parameter code 82398
98 USGS
99 USGS
100 USGS
101 USGS
102 USGS
103 USGS
104 USGS
105 USGS
106 USGS
107 USGS
108 USGS
109 USGS
110 USGS
111 USGS
112 USGS
113 USGS
114 USGS
115 USGS
116 USGS
117 USGS
118 USGS
119 USGS
120 USGS
121 USGS
122 USGS
123 USGS
124 USGS
125 USGS
126 USGS
127 USGS
128 USGS
129 USGS
130 USGS
131 USGS
132 USGS
133 USGS
134 USGS
135 USGS
136 USGS
137 USGS
138 USGS
139 USGS
140 USGS
141 USGS
142 USGS
143 USGS
144 USGS
145 USGS
146 USGS
147 USGS
148 USGS
149 USGS
150 USGS
151 USGS
SampleCollectionMethod.MethodName
1 USGS
2 USGS
3 USGS
4 USGS
5 USGS
6 USGS
7 USGS
8 USGS
9 USGS
10 USGS
11 USGS
12 USGS
13 USGS
14 USGS
15 USGS
16 USGS
17 USGS
18 USGS
19 USGS
20 USGS
21 USGS
22 USGS
23 USGS
24 USGS
25 USGS
26 USGS
27 USGS
28 USGS
29 USGS
30 USGS
31 USGS
32 USGS
33 USGS
34 USGS
35 USGS
36 USGS
37 USGS
38 USGS
39 USGS
40 USGS
41 USGS
42 USGS
43 USGS
44 USGS
45 USGS
46 USGS
47 USGS
48 USGS
49 USGS
50 USGS
51 USGS
52 USGS
53 USGS
54 USGS
55 USGS
56 USGS
57 USGS
58 Grab sample (dip)
59 USGS
60 USGS
61 USGS
62 USGS
63 USGS
64 USGS
65 USGS
66 USGS
67 USGS
68 USGS
69 USGS
70 Grab sample (dip)
71 USGS
72 USGS
73 USGS
74 USGS
75 USGS
76 USGS
77 USGS
78 USGS
79 USGS
80 Multiple verticals
81 Grab sample (dip)
82 Grab sample (dip)
83 Grab sample (dip)
84 Multiple verticals
85 Grab sample (dip)
86 USGS
87 USGS
88 Grab sample (dip)
89 Grab sample (dip)
90 Grab sample (dip)
91 USGS
92 USGS
93 USGS
94 Grab sample (dip)
95 USGS
96 Grab sample (dip)
97 Grab sample (dip)
98 USGS
99 USGS
100 USGS
101 USGS
102 USGS
103 USGS
104 USGS
105 USGS
106 USGS
107 USGS
108 USGS
109 USGS
110 USGS
111 USGS
112 USGS
113 USGS
114 USGS
115 USGS
116 USGS
117 USGS
118 USGS
119 USGS
120 USGS
121 USGS
122 USGS
123 USGS
124 USGS
125 USGS
126 USGS
127 USGS
128 USGS
129 USGS
130 USGS
131 USGS
132 USGS
133 USGS
134 USGS
135 USGS
136 USGS
137 USGS
138 USGS
139 USGS
140 USGS
141 USGS
142 USGS
143 USGS
144 USGS
145 USGS
146 USGS
147 USGS
148 USGS
149 USGS
150 USGS
151 USGS
SampleCollectionMethod.MethodDescriptionText
1 <NA>
2 <NA>
3 <NA>
4 <NA>
5 <NA>
6 <NA>
7 <NA>
8 <NA>
9 <NA>
10 <NA>
11 <NA>
12 <NA>
13 <NA>
14 <NA>
15 <NA>
16 <NA>
17 <NA>
18 <NA>
19 <NA>
20 <NA>
21 <NA>
22 <NA>
23 <NA>
24 <NA>
25 <NA>
26 <NA>
27 <NA>
28 <NA>
29 <NA>
30 <NA>
31 <NA>
32 <NA>
33 <NA>
34 <NA>
35 <NA>
36 <NA>
37 <NA>
38 <NA>
39 <NA>
40 <NA>
41 <NA>
42 <NA>
43 <NA>
44 <NA>
45 <NA>
46 <NA>
47 <NA>
48 <NA>
49 <NA>
50 <NA>
51 <NA>
52 <NA>
53 <NA>
54 <NA>
55 <NA>
56 <NA>
57 <NA>
58 <NA>
59 <NA>
60 <NA>
61 <NA>
62 <NA>
63 <NA>
64 <NA>
65 <NA>
66 <NA>
67 <NA>
68 <NA>
69 <NA>
70 <NA>
71 <NA>
72 <NA>
73 <NA>
74 <NA>
75 <NA>
76 <NA>
77 <NA>
78 <NA>
79 <NA>
80 <NA>
81 <NA>
82 <NA>
83 <NA>
84 <NA>
85 <NA>
86 <NA>
87 <NA>
88 <NA>
89 <NA>
90 <NA>
91 <NA>
92 <NA>
93 <NA>
94 <NA>
95 <NA>
96 <NA>
97 <NA>
98 <NA>
99 <NA>
100 <NA>
101 <NA>
102 <NA>
103 <NA>
104 <NA>
105 <NA>
106 <NA>
107 <NA>
108 <NA>
109 <NA>
110 <NA>
111 <NA>
112 <NA>
113 <NA>
114 <NA>
115 <NA>
116 <NA>
117 <NA>
118 <NA>
119 <NA>
120 <NA>
121 <NA>
122 <NA>
123 <NA>
124 <NA>
125 <NA>
126 <NA>
127 <NA>
128 <NA>
129 <NA>
130 <NA>
131 <NA>
132 <NA>
133 <NA>
134 <NA>
135 <NA>
136 <NA>
137 <NA>
138 <NA>
139 <NA>
140 <NA>
141 <NA>
142 <NA>
143 <NA>
144 <NA>
145 <NA>
146 <NA>
147 <NA>
148 <NA>
149 <NA>
150 <NA>
151 <NA>
SampleCollectionEquipmentName ResultIdentifier
1 Unknown NWIS-30088805
2 Unknown NWIS-30088806
3 Unknown NWIS-30182074
4 Unknown NWIS-30180150
5 Unknown NWIS-30180151
6 Unknown NWIS-30161894
7 Unknown NWIS-30218801
8 Unknown NWIS-30218802
9 Unknown NWIS-30208413
10 Unknown NWIS-30208414
11 Unknown NWIS-30199411
12 Unknown NWIS-30199412
13 Unknown NWIS-90573353
14 Unknown NWIS-90491826
15 Unknown NWIS-30196947
16 Unknown NWIS-30196948
17 Unknown NWIS-30198658
18 Unknown NWIS-30198659
19 Unknown NWIS-30218730
20 Unknown NWIS-30218731
21 Unknown NWIS-30218708
22 Unknown NWIS-30218709
23 Unknown NWIS-30198804
24 Unknown NWIS-30198805
25 Unknown NWIS-30218774
26 Unknown NWIS-30218775
27 Unknown NWIS-30212043
28 Unknown NWIS-30212044
29 Unknown NWIS-30218637
30 Unknown NWIS-30218638
31 Unknown NWIS-30218844
32 Unknown NWIS-30218845
33 Unknown NWIS-30218752
34 Unknown NWIS-30218753
35 Unknown NWIS-30218659
36 Unknown NWIS-30218660
37 Unknown NWIS-30254539
38 Unknown NWIS-30255276
39 Unknown NWIS-78545257
40 Unknown NWIS-78545258
41 Unknown NWIS-82113919
42 Unknown NWIS-90491827
43 Unknown NWIS-90491810
44 Unknown NWIS-90491811
45 Unknown NWIS-30297640
46 Unknown NWIS-30297641
47 Unknown NWIS-92971736
48 Unknown NWIS-92971753
49 Unknown NWIS-92971754
50 Unknown NWIS-92808325
51 Unknown NWIS-30297396
52 Unknown NWIS-30297397
53 Unknown NWIS-30297510
54 Unknown NWIS-30297848
55 Unknown NWIS-30297849
56 Unknown NWIS-29529639
57 Unknown NWIS-29528583
58 Grab sample NWIS-101049900
59 Unknown NWIS-30297961
60 Unknown NWIS-30297962
61 Unknown NWIS-29500735
62 Unknown NWIS-29501837
63 Unknown NWIS-29501854
64 Unknown NWIS-29510965
65 Unknown NWIS-30297509
66 Unknown NWIS-90573352
67 Unknown NWIS-29501940
68 Unknown NWIS-29528597
69 Unknown NWIS-29529625
70 Grab sample NWIS-101282547
71 Unknown NWIS-29530569
72 Unknown NWIS-29529611
73 Unknown NWIS-30297527
74 Unknown NWIS-30297528
75 Unknown NWIS-29547156
76 Unknown NWIS-29547170
77 Unknown NWIS-29547613
78 Unknown NWIS-29547435
79 Unknown NWIS-29501870
80 US DH-81 with Teflon cap and nozzle NWIS-101048361
81 Grab sample NWIS-101366986
82 Grab sample NWIS-100781708
83 Grab sample NWIS-100603239
84 US DH-81 with Teflon cap and nozzle NWIS-101133702
85 Grab sample NWIS-101283154
86 Unknown NWIS-92971735
87 Unknown NWIS-29504580
88 Grab sample NWIS-103942215
89 Grab sample NWIS-100546867
90 Grab sample NWIS-100924037
91 Unknown NWIS-92808326
92 Unknown NWIS-29547575
93 Unknown NWIS-29547627
94 Grab sample NWIS-101134907
95 Unknown NWIS-29501821
96 Grab sample NWIS-100740818
97 Grab sample NWIS-100666550
98 Unknown NWIS-30293801
99 Unknown NWIS-30309221
100 Unknown NWIS-30230362
101 Unknown NWIS-30230363
102 Unknown NWIS-30309449
103 Unknown NWIS-30291950
104 Unknown NWIS-30309222
105 Unknown NWIS-30291837
106 Unknown NWIS-30291949
107 Unknown NWIS-30293800
108 Unknown NWIS-30293574
109 Unknown NWIS-30292664
110 Unknown NWIS-30292665
111 Unknown NWIS-30292534
112 Unknown NWIS-30309676
113 Unknown NWIS-30291836
114 Unknown NWIS-30292533
115 Unknown NWIS-30309883
116 Unknown NWIS-30309884
117 Unknown NWIS-30293157
118 Unknown NWIS-30293158
119 Unknown NWIS-30293365
120 Unknown NWIS-30293366
121 Unknown NWIS-30309336
122 Unknown NWIS-30309337
123 Unknown NWIS-89513211
124 Unknown NWIS-89505609
125 Unknown NWIS-89505610
126 Unknown NWIS-30291967
127 Unknown NWIS-30291968
128 Unknown NWIS-30291630
129 Unknown NWIS-30292306
130 Unknown NWIS-30292307
131 Unknown NWIS-30291611
132 Unknown NWIS-30292515
133 Unknown NWIS-30292516
134 Unknown NWIS-30293573
135 Unknown NWIS-89513210
136 Unknown NWIS-30292081
137 Unknown NWIS-30297282
138 Unknown NWIS-30297283
139 Unknown NWIS-30309450
140 Unknown NWIS-30291629
141 Unknown NWIS-30293591
142 Unknown NWIS-30293592
143 Unknown NWIS-30292288
144 Unknown NWIS-30292289
145 Unknown NWIS-30291612
146 Unknown NWIS-30292552
147 Unknown NWIS-30292553
148 Unknown NWIS-30292080
149 Unknown NWIS-30309562
150 Unknown NWIS-30309563
151 Unknown NWIS-30309675
ResultDetectionConditionText MethodSpeciationName CharacteristicName
1 <NA> <NA> Phosphorus
2 <NA> <NA> Phosphorus
3 <NA> <NA> Phosphorus
4 <NA> <NA> Phosphorus
5 <NA> <NA> Phosphorus
6 <NA> <NA> Phosphorus
7 <NA> <NA> Phosphorus
8 <NA> <NA> Phosphorus
9 <NA> <NA> Phosphorus
10 <NA> <NA> Phosphorus
11 <NA> <NA> Phosphorus
12 <NA> <NA> Phosphorus
13 <NA> <NA> Phosphorus
14 <NA> <NA> Phosphorus
15 <NA> <NA> Phosphorus
16 <NA> <NA> Phosphorus
17 <NA> <NA> Phosphorus
18 <NA> <NA> Phosphorus
19 <NA> <NA> Phosphorus
20 <NA> <NA> Phosphorus
21 <NA> <NA> Phosphorus
22 <NA> <NA> Phosphorus
23 <NA> <NA> Phosphorus
24 <NA> <NA> Phosphorus
25 <NA> <NA> Phosphorus
26 <NA> <NA> Phosphorus
27 <NA> <NA> Phosphorus
28 <NA> <NA> Phosphorus
29 <NA> <NA> Phosphorus
30 <NA> <NA> Phosphorus
31 <NA> <NA> Phosphorus
32 <NA> <NA> Phosphorus
33 <NA> <NA> Phosphorus
34 <NA> <NA> Phosphorus
35 <NA> <NA> Phosphorus
36 <NA> <NA> Phosphorus
37 <NA> <NA> Phosphorus
38 <NA> <NA> Phosphorus
39 <NA> <NA> Phosphorus
40 <NA> <NA> Phosphorus
41 <NA> <NA> Phosphorus
42 <NA> <NA> Phosphorus
43 <NA> <NA> Phosphorus
44 <NA> <NA> Phosphorus
45 <NA> <NA> Phosphorus
46 <NA> <NA> Phosphorus
47 <NA> <NA> Phosphorus
48 <NA> <NA> Phosphorus
49 <NA> <NA> Phosphorus
50 <NA> <NA> Phosphorus
51 <NA> <NA> Phosphorus
52 <NA> <NA> Phosphorus
53 Not Detected <NA> Phosphorus
54 <NA> <NA> Phosphorus
55 Not Detected <NA> Phosphorus
56 <NA> <NA> Phosphorus
57 <NA> <NA> Phosphorus
58 <NA> <NA> Phosphorus
59 <NA> <NA> Phosphorus
60 Not Detected <NA> Phosphorus
61 <NA> <NA> Phosphorus
62 <NA> <NA> Phosphorus
63 <NA> <NA> Phosphorus
64 <NA> <NA> Phosphorus
65 Not Detected <NA> Phosphorus
66 <NA> <NA> Phosphorus
67 <NA> <NA> Phosphorus
68 <NA> <NA> Phosphorus
69 <NA> <NA> Phosphorus
70 <NA> <NA> Phosphorus
71 <NA> <NA> Phosphorus
72 <NA> <NA> Phosphorus
73 Not Detected <NA> Phosphorus
74 Not Detected <NA> Phosphorus
75 <NA> <NA> Phosphorus
76 <NA> <NA> Phosphorus
77 <NA> <NA> Phosphorus
78 <NA> <NA> Phosphorus
79 <NA> <NA> Phosphorus
80 <NA> <NA> Phosphorus
81 <NA> <NA> Phosphorus
82 <NA> <NA> Phosphorus
83 <NA> <NA> Phosphorus
84 <NA> <NA> Phosphorus
85 <NA> <NA> Phosphorus
86 <NA> <NA> Phosphorus
87 <NA> <NA> Phosphorus
88 <NA> <NA> Phosphorus
89 <NA> <NA> Phosphorus
90 <NA> <NA> Phosphorus
91 <NA> <NA> Phosphorus
92 <NA> <NA> Phosphorus
93 <NA> <NA> Phosphorus
94 <NA> <NA> Phosphorus
95 <NA> <NA> Phosphorus
96 <NA> <NA> Phosphorus
97 <NA> <NA> Phosphorus
98 <NA> <NA> Phosphorus
99 <NA> <NA> Phosphorus
100 <NA> <NA> Phosphorus
101 <NA> <NA> Phosphorus
102 <NA> <NA> Phosphorus
103 <NA> <NA> Phosphorus
104 Not Detected <NA> Phosphorus
105 Not Detected <NA> Phosphorus
106 <NA> <NA> Phosphorus
107 <NA> <NA> Phosphorus
108 <NA> <NA> Phosphorus
109 <NA> <NA> Phosphorus
110 Not Detected <NA> Phosphorus
111 <NA> <NA> Phosphorus
112 Not Detected <NA> Phosphorus
113 <NA> <NA> Phosphorus
114 <NA> <NA> Phosphorus
115 <NA> <NA> Phosphorus
116 <NA> <NA> Phosphorus
117 <NA> <NA> Phosphorus
118 <NA> <NA> Phosphorus
119 <NA> <NA> Phosphorus
120 Not Detected <NA> Phosphorus
121 Not Detected <NA> Phosphorus
122 Not Detected <NA> Phosphorus
123 <NA> <NA> Phosphorus
124 <NA> <NA> Phosphorus
125 <NA> <NA> Phosphorus
126 <NA> <NA> Phosphorus
127 <NA> <NA> Phosphorus
128 <NA> <NA> Phosphorus
129 <NA> <NA> Phosphorus
130 Not Detected <NA> Phosphorus
131 <NA> <NA> Phosphorus
132 <NA> <NA> Phosphorus
133 <NA> <NA> Phosphorus
134 <NA> <NA> Phosphorus
135 <NA> <NA> Phosphorus
136 Not Detected <NA> Phosphorus
137 <NA> <NA> Phosphorus
138 <NA> <NA> Phosphorus
139 <NA> <NA> Phosphorus
140 <NA> <NA> Phosphorus
141 <NA> <NA> Phosphorus
142 <NA> <NA> Phosphorus
143 <NA> <NA> Phosphorus
144 Not Detected <NA> Phosphorus
145 <NA> <NA> Phosphorus
146 <NA> <NA> Phosphorus
147 Not Detected <NA> Phosphorus
148 <NA> <NA> Phosphorus
149 <NA> <NA> Phosphorus
150 Not Detected <NA> Phosphorus
151 <NA> <NA> Phosphorus
ResultSampleFractionText ResultMeasureValue ResultMeasure.MeasureUnitCode
1 Total 0.130 mg/l as P
2 Dissolved 0.050 mg/l as P
3 Dissolved 0.780 mg/l as P
4 Total 0.110 mg/l as P
5 Dissolved 0.090 mg/l as P
6 Dissolved 0.320 mg/l as P
7 Total 0.041 mg/l as P
8 Dissolved 0.026 mg/l as P
9 Total 0.060 mg/l as P
10 Dissolved 0.020 mg/l as P
11 Total 0.140 mg/l as P
12 Dissolved 0.080 mg/l as P
13 Dissolved 0.011 mg/l as P
14 Total 0.039 mg/l as P
15 Total 0.020 mg/l as P
16 Dissolved 0.020 mg/l as P
17 Total 0.030 mg/l as P
18 Dissolved 0.020 mg/l as P
19 Total 0.053 mg/l as P
20 Dissolved 0.038 mg/l as P
21 Total 0.031 mg/l as P
22 Dissolved 0.016 mg/l as P
23 Total 0.020 mg/l as P
24 Dissolved 0.020 mg/l as P
25 Total 0.051 mg/l as P
26 Dissolved 0.050 mg/l as P
27 Total 0.040 mg/l as P
28 Dissolved 0.020 mg/l as P
29 Total 0.033 mg/l as P
30 Dissolved 0.023 mg/l as P
31 Total 0.027 mg/l as P
32 Dissolved 0.018 mg/l as P
33 Total 0.039 mg/l as P
34 Dissolved 0.028 mg/l as P
35 Total 0.036 mg/l as P
36 Dissolved 0.022 mg/l as P
37 Total 0.030 mg/l as P
38 Total 0.030 mg/l as P
39 Total 0.132 mg/l as P
40 Dissolved 0.094 mg/l as P
41 Total 0.580 mg/l as P
42 Dissolved 0.022 mg/l as P
43 Total 0.058 mg/l as P
44 Dissolved 0.030 mg/l as P
45 Total 0.090 mg/l as P
46 Dissolved 0.040 mg/l as P
47 Dissolved 0.019 mg/l as P
48 Total 0.028 mg/l as P
49 Dissolved 0.016 mg/l as P
50 Total 0.043 mg/l as P
51 Total 0.130 mg/l as P
52 Dissolved 0.040 mg/l as P
53 Dissolved NA <NA>
54 Total 0.020 mg/l as P
55 Dissolved NA <NA>
56 Total 0.080 mg/l as P
57 Total 0.046 mg/l as P
58 Total 0.083 mg/l as P
59 Total 0.030 mg/l as P
60 Dissolved NA <NA>
61 Total 0.038 mg/l as P
62 Total 0.036 mg/l as P
63 Total 0.390 mg/l as P
64 Total 0.075 mg/l as P
65 Total NA <NA>
66 Total 0.035 mg/l as P
67 Total 0.032 mg/l as P
68 Total 0.077 mg/l as P
69 Total 0.128 mg/l as P
70 Total 0.166 mg/l as P
71 Total 0.350 mg/l as P
72 Total 0.057 mg/l as P
73 Total NA <NA>
74 Dissolved NA <NA>
75 Total 0.075 mg/l as P
76 Total 0.037 mg/l as P
77 Total 0.450 mg/l as P
78 Total 0.060 mg/l as P
79 Total 0.090 mg/l as P
80 Total 0.032 mg/l as P
81 Total 0.033 mg/l as P
82 Total 0.080 mg/l as P
83 Total 0.056 mg/l as P
84 Total 0.032 mg/l as P
85 Total 0.037 mg/l as P
86 Total 0.064 mg/l as P
87 Total 0.111 mg/l as P
88 Total 0.113 mg/l as P
89 Total 0.040 mg/l as P
90 Total 0.078 mg/l as P
91 Dissolved 0.016 mg/l as P
92 Total 0.050 mg/l as P
93 Total 0.054 mg/l as P
94 Total 0.082 mg/l as P
95 Total 0.043 mg/l as P
96 Total 0.062 mg/l as P
97 Total 0.058 mg/l as P
98 Dissolved 0.060 mg/l as P
99 Total 0.040 mg/l as P
100 Total 0.110 mg/l as P
101 Dissolved 0.040 mg/l as P
102 Total 0.070 mg/l as P
103 Dissolved 0.050 mg/l as P
104 Dissolved NA <NA>
105 Dissolved NA <NA>
106 Total 0.120 mg/l as P
107 Total 0.060 mg/l as P
108 Dissolved 0.060 mg/l as P
109 Total 0.020 mg/l as P
110 Dissolved NA <NA>
111 Dissolved 0.060 mg/l as P
112 Dissolved NA <NA>
113 Total 0.030 mg/l as P
114 Total 0.120 mg/l as P
115 Total 0.060 mg/l as P
116 Dissolved 0.040 mg/l as P
117 Total 0.020 mg/l as P
118 Dissolved 0.010 mg/l as P
119 Total 0.030 mg/l as P
120 Dissolved NA <NA>
121 Total NA <NA>
122 Dissolved NA <NA>
123 Dissolved 0.024 mg/l as P
124 Total 0.168 mg/l as P
125 Dissolved 0.037 mg/l as P
126 Total 0.110 mg/l as P
127 Dissolved 0.030 mg/l as P
128 Dissolved 0.020 mg/l as P
129 Total 0.020 mg/l as P
130 Dissolved NA <NA>
131 Total 0.020 mg/l as P
132 Total 0.090 mg/l as P
133 Dissolved 0.040 mg/l as P
134 Total 0.130 mg/l as P
135 Total 0.135 mg/l as P
136 Dissolved NA <NA>
137 Total 0.130 mg/l as P
138 Dissolved 0.050 mg/l as P
139 Dissolved 0.050 mg/l as P
140 Total 0.110 mg/l as P
141 Total 0.030 mg/l as P
142 Dissolved 0.010 mg/l as P
143 Total 0.070 mg/l as P
144 Dissolved NA <NA>
145 Dissolved 0.020 mg/l as P
146 Total 0.020 mg/l as P
147 Dissolved NA <NA>
148 Total 0.020 mg/l as P
149 Total 0.050 mg/l as P
150 Dissolved NA <NA>
151 Total 0.030 mg/l as P
MeasureQualifierCode ResultStatusIdentifier StatisticalBaseCode
1 <NA> Historical <NA>
2 <NA> Historical <NA>
3 <NA> Historical <NA>
4 <NA> Historical <NA>
5 <NA> Historical <NA>
6 <NA> Historical <NA>
7 <NA> Historical <NA>
8 <NA> Historical <NA>
9 <NA> Historical <NA>
10 <NA> Historical <NA>
11 <NA> Historical <NA>
12 <NA> Historical <NA>
13 <NA> Accepted <NA>
14 <NA> Accepted <NA>
15 <NA> Historical <NA>
16 <NA> Historical <NA>
17 <NA> Historical <NA>
18 <NA> Historical <NA>
19 <NA> Historical <NA>
20 <NA> Historical <NA>
21 <NA> Historical <NA>
22 <NA> Historical <NA>
23 <NA> Historical <NA>
24 <NA> Historical <NA>
25 <NA> Historical <NA>
26 <NA> Historical <NA>
27 <NA> Historical <NA>
28 <NA> Historical <NA>
29 <NA> Historical <NA>
30 <NA> Historical <NA>
31 <NA> Historical <NA>
32 <NA> Historical <NA>
33 <NA> Historical <NA>
34 <NA> Historical <NA>
35 <NA> Historical <NA>
36 <NA> Historical <NA>
37 <NA> Historical <NA>
38 <NA> Historical <NA>
39 <NA> Accepted <NA>
40 <NA> Accepted <NA>
41 <NA> Accepted <NA>
42 <NA> Accepted <NA>
43 <NA> Accepted <NA>
44 <NA> Accepted <NA>
45 <NA> Historical <NA>
46 <NA> Historical <NA>
47 <NA> Accepted <NA>
48 <NA> Accepted <NA>
49 <NA> Accepted <NA>
50 <NA> Accepted <NA>
51 <NA> Historical <NA>
52 <NA> Historical <NA>
53 <NA> Historical <NA>
54 <NA> Historical <NA>
55 <NA> Historical <NA>
56 <NA> Accepted <NA>
57 <NA> Accepted <NA>
58 <NA> Accepted <NA>
59 <NA> Historical <NA>
60 <NA> Historical <NA>
61 <NA> Accepted <NA>
62 <NA> Accepted <NA>
63 <NA> Accepted <NA>
64 <NA> Accepted <NA>
65 <NA> Historical <NA>
66 <NA> Accepted <NA>
67 <NA> Accepted <NA>
68 <NA> Accepted <NA>
69 <NA> Accepted <NA>
70 <NA> Accepted <NA>
71 <NA> Accepted <NA>
72 <NA> Accepted <NA>
73 <NA> Historical <NA>
74 <NA> Historical <NA>
75 <NA> Accepted <NA>
76 <NA> Accepted <NA>
77 <NA> Accepted <NA>
78 <NA> Accepted <NA>
79 <NA> Accepted <NA>
80 <NA> Accepted <NA>
81 <NA> Accepted <NA>
82 <NA> Accepted <NA>
83 <NA> Accepted <NA>
84 <NA> Accepted <NA>
85 <NA> Accepted <NA>
86 <NA> Accepted <NA>
87 <NA> Accepted <NA>
88 <NA> Accepted <NA>
89 <NA> Accepted <NA>
90 <NA> Accepted <NA>
91 <NA> Accepted <NA>
92 <NA> Accepted <NA>
93 <NA> Accepted <NA>
94 <NA> Accepted <NA>
95 <NA> Accepted <NA>
96 <NA> Accepted <NA>
97 <NA> Accepted <NA>
98 <NA> Historical <NA>
99 <NA> Historical <NA>
100 <NA> Historical <NA>
101 <NA> Historical <NA>
102 <NA> Historical <NA>
103 <NA> Historical <NA>
104 <NA> Historical <NA>
105 <NA> Historical <NA>
106 <NA> Historical <NA>
107 <NA> Historical <NA>
108 <NA> Historical <NA>
109 <NA> Historical <NA>
110 <NA> Historical <NA>
111 <NA> Historical <NA>
112 <NA> Historical <NA>
113 <NA> Historical <NA>
114 <NA> Historical <NA>
115 <NA> Historical <NA>
116 <NA> Historical <NA>
117 <NA> Historical <NA>
118 <NA> Historical <NA>
119 <NA> Historical <NA>
120 <NA> Historical <NA>
121 <NA> Historical <NA>
122 <NA> Historical <NA>
123 <NA> Accepted <NA>
124 <NA> Accepted <NA>
125 <NA> Accepted <NA>
126 <NA> Historical <NA>
127 <NA> Historical <NA>
128 <NA> Historical <NA>
129 <NA> Historical <NA>
130 <NA> Historical <NA>
131 <NA> Historical <NA>
132 <NA> Historical <NA>
133 <NA> Historical <NA>
134 <NA> Historical <NA>
135 <NA> Accepted <NA>
136 <NA> Historical <NA>
137 <NA> Historical <NA>
138 <NA> Historical <NA>
139 <NA> Historical <NA>
140 <NA> Historical <NA>
141 <NA> Historical <NA>
142 <NA> Historical <NA>
143 <NA> Historical <NA>
144 <NA> Historical <NA>
145 <NA> Historical <NA>
146 <NA> Historical <NA>
147 <NA> Historical <NA>
148 <NA> Historical <NA>
149 <NA> Historical <NA>
150 <NA> Historical <NA>
151 <NA> Historical <NA>
ResultValueTypeName ResultWeightBasisText ResultTimeBasisText
1 Actual <NA> <NA>
2 Actual <NA> <NA>
3 Actual <NA> <NA>
4 Actual <NA> <NA>
5 Actual <NA> <NA>
6 Actual <NA> <NA>
7 Actual <NA> <NA>
8 Actual <NA> <NA>
9 Actual <NA> <NA>
10 Actual <NA> <NA>
11 Actual <NA> <NA>
12 Actual <NA> <NA>
13 Actual <NA> <NA>
14 Actual <NA> <NA>
15 Actual <NA> <NA>
16 Actual <NA> <NA>
17 Actual <NA> <NA>
18 Actual <NA> <NA>
19 Actual <NA> <NA>
20 Actual <NA> <NA>
21 Actual <NA> <NA>
22 Actual <NA> <NA>
23 Actual <NA> <NA>
24 Actual <NA> <NA>
25 Actual <NA> <NA>
26 Actual <NA> <NA>
27 Actual <NA> <NA>
28 Actual <NA> <NA>
29 Actual <NA> <NA>
30 Actual <NA> <NA>
31 Actual <NA> <NA>
32 Actual <NA> <NA>
33 Actual <NA> <NA>
34 Actual <NA> <NA>
35 Actual <NA> <NA>
36 Actual <NA> <NA>
37 Actual <NA> <NA>
38 Actual <NA> <NA>
39 Actual <NA> <NA>
40 Actual <NA> <NA>
41 Actual <NA> <NA>
42 Actual <NA> <NA>
43 Actual <NA> <NA>
44 Actual <NA> <NA>
45 Actual <NA> <NA>
46 Actual <NA> <NA>
47 Actual <NA> <NA>
48 Actual <NA> <NA>
49 Actual <NA> <NA>
50 Actual <NA> <NA>
51 Actual <NA> <NA>
52 Actual <NA> <NA>
53 Actual <NA> <NA>
54 Actual <NA> <NA>
55 Actual <NA> <NA>
56 Actual <NA> <NA>
57 Actual <NA> <NA>
58 Actual <NA> <NA>
59 Actual <NA> <NA>
60 Actual <NA> <NA>
61 Actual <NA> <NA>
62 Actual <NA> <NA>
63 Actual <NA> <NA>
64 Actual <NA> <NA>
65 Actual <NA> <NA>
66 Actual <NA> <NA>
67 Actual <NA> <NA>
68 Actual <NA> <NA>
69 Actual <NA> <NA>
70 Actual <NA> <NA>
71 Actual <NA> <NA>
72 Actual <NA> <NA>
73 Actual <NA> <NA>
74 Actual <NA> <NA>
75 Actual <NA> <NA>
76 Actual <NA> <NA>
77 Actual <NA> <NA>
78 Actual <NA> <NA>
79 Actual <NA> <NA>
80 Actual <NA> <NA>
81 Actual <NA> <NA>
82 Actual <NA> <NA>
83 Actual <NA> <NA>
84 Actual <NA> <NA>
85 Actual <NA> <NA>
86 Actual <NA> <NA>
87 Actual <NA> <NA>
88 Actual <NA> <NA>
89 Actual <NA> <NA>
90 Actual <NA> <NA>
91 Actual <NA> <NA>
92 Actual <NA> <NA>
93 Actual <NA> <NA>
94 Actual <NA> <NA>
95 Actual <NA> <NA>
96 Actual <NA> <NA>
97 Actual <NA> <NA>
98 Actual <NA> <NA>
99 Estimated <NA> <NA>
100 Actual <NA> <NA>
101 Actual <NA> <NA>
102 Actual <NA> <NA>
103 Actual <NA> <NA>
104 Actual <NA> <NA>
105 Actual <NA> <NA>
106 Actual <NA> <NA>
107 Actual <NA> <NA>
108 Actual <NA> <NA>
109 Actual <NA> <NA>
110 Actual <NA> <NA>
111 Actual <NA> <NA>
112 Actual <NA> <NA>
113 Actual <NA> <NA>
114 Actual <NA> <NA>
115 Actual <NA> <NA>
116 Estimated <NA> <NA>
117 Actual <NA> <NA>
118 Actual <NA> <NA>
119 Actual <NA> <NA>
120 Actual <NA> <NA>
121 Actual <NA> <NA>
122 Actual <NA> <NA>
123 Actual <NA> <NA>
124 Actual <NA> <NA>
125 Actual <NA> <NA>
126 Actual <NA> <NA>
127 Actual <NA> <NA>
128 Actual <NA> <NA>
129 Actual <NA> <NA>
130 Actual <NA> <NA>
131 Actual <NA> <NA>
132 Actual <NA> <NA>
133 Actual <NA> <NA>
134 Actual <NA> <NA>
135 Actual <NA> <NA>
136 Actual <NA> <NA>
137 Actual <NA> <NA>
138 Actual <NA> <NA>
139 Actual <NA> <NA>
140 Actual <NA> <NA>
141 Actual <NA> <NA>
142 Actual <NA> <NA>
143 Actual <NA> <NA>
144 Actual <NA> <NA>
145 Actual <NA> <NA>
146 Actual <NA> <NA>
147 Actual <NA> <NA>
148 Actual <NA> <NA>
149 Actual <NA> <NA>
150 Actual <NA> <NA>
151 Estimated <NA> <NA>
ResultTemperatureBasisText ResultParticleSizeBasisText
1 <NA> <NA>
2 <NA> <NA>
3 <NA> <NA>
4 <NA> <NA>
5 <NA> <NA>
6 <NA> <NA>
7 <NA> <NA>
8 <NA> <NA>
9 <NA> <NA>
10 <NA> <NA>
11 <NA> <NA>
12 <NA> <NA>
13 <NA> <NA>
14 <NA> <NA>
15 <NA> <NA>
16 <NA> <NA>
17 <NA> <NA>
18 <NA> <NA>
19 <NA> <NA>
20 <NA> <NA>
21 <NA> <NA>
22 <NA> <NA>
23 <NA> <NA>
24 <NA> <NA>
25 <NA> <NA>
26 <NA> <NA>
27 <NA> <NA>
28 <NA> <NA>
29 <NA> <NA>
30 <NA> <NA>
31 <NA> <NA>
32 <NA> <NA>
33 <NA> <NA>
34 <NA> <NA>
35 <NA> <NA>
36 <NA> <NA>
37 <NA> <NA>
38 <NA> <NA>
39 <NA> <NA>
40 <NA> <NA>
41 <NA> <NA>
42 <NA> <NA>
43 <NA> <NA>
44 <NA> <NA>
45 <NA> <NA>
46 <NA> <NA>
47 <NA> <NA>
48 <NA> <NA>
49 <NA> <NA>
50 <NA> <NA>
51 <NA> <NA>
52 <NA> <NA>
53 <NA> <NA>
54 <NA> <NA>
55 <NA> <NA>
56 <NA> <NA>
57 <NA> <NA>
58 <NA> <NA>
59 <NA> <NA>
60 <NA> <NA>
61 <NA> <NA>
62 <NA> <NA>
63 <NA> <NA>
64 <NA> <NA>
65 <NA> <NA>
66 <NA> <NA>
67 <NA> <NA>
68 <NA> <NA>
69 <NA> <NA>
70 <NA> <NA>
71 <NA> <NA>
72 <NA> <NA>
73 <NA> <NA>
74 <NA> <NA>
75 <NA> <NA>
76 <NA> <NA>
77 <NA> <NA>
78 <NA> <NA>
79 <NA> <NA>
80 <NA> <NA>
81 <NA> <NA>
82 <NA> <NA>
83 <NA> <NA>
84 <NA> <NA>
85 <NA> <NA>
86 <NA> <NA>
87 <NA> <NA>
88 <NA> <NA>
89 <NA> <NA>
90 <NA> <NA>
91 <NA> <NA>
92 <NA> <NA>
93 <NA> <NA>
94 <NA> <NA>
95 <NA> <NA>
96 <NA> <NA>
97 <NA> <NA>
98 <NA> <NA>
99 <NA> <NA>
100 <NA> <NA>
101 <NA> <NA>
102 <NA> <NA>
103 <NA> <NA>
104 <NA> <NA>
105 <NA> <NA>
106 <NA> <NA>
107 <NA> <NA>
108 <NA> <NA>
109 <NA> <NA>
110 <NA> <NA>
111 <NA> <NA>
112 <NA> <NA>
113 <NA> <NA>
114 <NA> <NA>
115 <NA> <NA>
116 <NA> <NA>
117 <NA> <NA>
118 <NA> <NA>
119 <NA> <NA>
120 <NA> <NA>
121 <NA> <NA>
122 <NA> <NA>
123 <NA> <NA>
124 <NA> <NA>
125 <NA> <NA>
126 <NA> <NA>
127 <NA> <NA>
128 <NA> <NA>
129 <NA> <NA>
130 <NA> <NA>
131 <NA> <NA>
132 <NA> <NA>
133 <NA> <NA>
134 <NA> <NA>
135 <NA> <NA>
136 <NA> <NA>
137 <NA> <NA>
138 <NA> <NA>
139 <NA> <NA>
140 <NA> <NA>
141 <NA> <NA>
142 <NA> <NA>
143 <NA> <NA>
144 <NA> <NA>
145 <NA> <NA>
146 <NA> <NA>
147 <NA> <NA>
148 <NA> <NA>
149 <NA> <NA>
150 <NA> <NA>
151 <NA> <NA>
DataQuality.PrecisionValue DataQuality.BiasValue
1 <NA> <NA>
2 <NA> <NA>
3 <NA> <NA>
4 <NA> <NA>
5 <NA> <NA>
6 <NA> <NA>
7 <NA> <NA>
8 <NA> <NA>
9 <NA> <NA>
10 <NA> <NA>
11 <NA> <NA>
12 <NA> <NA>
13 <NA> <NA>
14 <NA> <NA>
15 <NA> <NA>
16 <NA> <NA>
17 <NA> <NA>
18 <NA> <NA>
19 <NA> <NA>
20 <NA> <NA>
21 <NA> <NA>
22 <NA> <NA>
23 <NA> <NA>
24 <NA> <NA>
25 <NA> <NA>
26 <NA> <NA>
27 <NA> <NA>
28 <NA> <NA>
29 <NA> <NA>
30 <NA> <NA>
31 <NA> <NA>
32 <NA> <NA>
33 <NA> <NA>
34 <NA> <NA>
35 <NA> <NA>
36 <NA> <NA>
37 <NA> <NA>
38 <NA> <NA>
39 <NA> <NA>
40 <NA> <NA>
41 <NA> <NA>
42 <NA> <NA>
43 <NA> <NA>
44 <NA> <NA>
45 <NA> <NA>
46 <NA> <NA>
47 <NA> <NA>
48 <NA> <NA>
49 <NA> <NA>
50 <NA> <NA>
51 <NA> <NA>
52 <NA> <NA>
53 <NA> <NA>
54 <NA> <NA>
55 <NA> <NA>
56 <NA> <NA>
57 <NA> <NA>
58 <NA> <NA>
59 <NA> <NA>
60 <NA> <NA>
61 <NA> <NA>
62 <NA> <NA>
63 <NA> <NA>
64 <NA> <NA>
65 <NA> <NA>
66 <NA> <NA>
67 <NA> <NA>
68 <NA> <NA>
69 <NA> <NA>
70 <NA> <NA>
71 <NA> <NA>
72 <NA> <NA>
73 <NA> <NA>
74 <NA> <NA>
75 <NA> <NA>
76 <NA> <NA>
77 <NA> <NA>
78 <NA> <NA>
79 <NA> <NA>
80 <NA> <NA>
81 <NA> <NA>
82 <NA> <NA>
83 <NA> <NA>
84 <NA> <NA>
85 <NA> <NA>
86 <NA> <NA>
87 <NA> <NA>
88 <NA> <NA>
89 <NA> <NA>
90 <NA> <NA>
91 <NA> <NA>
92 <NA> <NA>
93 <NA> <NA>
94 <NA> <NA>
95 <NA> <NA>
96 <NA> <NA>
97 <NA> <NA>
98 <NA> <NA>
99 <NA> <NA>
100 <NA> <NA>
101 <NA> <NA>
102 <NA> <NA>
103 <NA> <NA>
104 <NA> <NA>
105 <NA> <NA>
106 <NA> <NA>
107 <NA> <NA>
108 <NA> <NA>
109 <NA> <NA>
110 <NA> <NA>
111 <NA> <NA>
112 <NA> <NA>
113 <NA> <NA>
114 <NA> <NA>
115 <NA> <NA>
116 <NA> <NA>
117 <NA> <NA>
118 <NA> <NA>
119 <NA> <NA>
120 <NA> <NA>
121 <NA> <NA>
122 <NA> <NA>
123 <NA> <NA>
124 <NA> <NA>
125 <NA> <NA>
126 <NA> <NA>
127 <NA> <NA>
128 <NA> <NA>
129 <NA> <NA>
130 <NA> <NA>
131 <NA> <NA>
132 <NA> <NA>
133 <NA> <NA>
134 <NA> <NA>
135 <NA> <NA>
136 <NA> <NA>
137 <NA> <NA>
138 <NA> <NA>
139 <NA> <NA>
140 <NA> <NA>
141 <NA> <NA>
142 <NA> <NA>
143 <NA> <NA>
144 <NA> <NA>
145 <NA> <NA>
146 <NA> <NA>
147 <NA> <NA>
148 <NA> <NA>
149 <NA> <NA>
150 <NA> <NA>
151 <NA> <NA>
DataQuality.ConfidenceIntervalValue DataQuality.UpperConfidenceLimitValue
1 <NA> <NA>
2 <NA> <NA>
3 <NA> <NA>
4 <NA> <NA>
5 <NA> <NA>
6 <NA> <NA>
7 <NA> <NA>
8 <NA> <NA>
9 <NA> <NA>
10 <NA> <NA>
11 <NA> <NA>
12 <NA> <NA>
13 <NA> <NA>
14 <NA> <NA>
15 <NA> <NA>
16 <NA> <NA>
17 <NA> <NA>
18 <NA> <NA>
19 <NA> <NA>
20 <NA> <NA>
21 <NA> <NA>
22 <NA> <NA>
23 <NA> <NA>
24 <NA> <NA>
25 <NA> <NA>
26 <NA> <NA>
27 <NA> <NA>
28 <NA> <NA>
29 <NA> <NA>
30 <NA> <NA>
31 <NA> <NA>
32 <NA> <NA>
33 <NA> <NA>
34 <NA> <NA>
35 <NA> <NA>
36 <NA> <NA>
37 <NA> <NA>
38 <NA> <NA>
39 <NA> <NA>
40 <NA> <NA>
41 <NA> <NA>
42 <NA> <NA>
43 <NA> <NA>
44 <NA> <NA>
45 <NA> <NA>
46 <NA> <NA>
47 <NA> <NA>
48 <NA> <NA>
49 <NA> <NA>
50 <NA> <NA>
51 <NA> <NA>
52 <NA> <NA>
53 <NA> <NA>
54 <NA> <NA>
55 <NA> <NA>
56 <NA> <NA>
57 <NA> <NA>
58 <NA> <NA>
59 <NA> <NA>
60 <NA> <NA>
61 <NA> <NA>
62 <NA> <NA>
63 <NA> <NA>
64 <NA> <NA>
65 <NA> <NA>
66 <NA> <NA>
67 <NA> <NA>
68 <NA> <NA>
69 <NA> <NA>
70 <NA> <NA>
71 <NA> <NA>
72 <NA> <NA>
73 <NA> <NA>
74 <NA> <NA>
75 <NA> <NA>
76 <NA> <NA>
77 <NA> <NA>
78 <NA> <NA>
79 <NA> <NA>
80 <NA> <NA>
81 <NA> <NA>
82 <NA> <NA>
83 <NA> <NA>
84 <NA> <NA>
85 <NA> <NA>
86 <NA> <NA>
87 <NA> <NA>
88 <NA> <NA>
89 <NA> <NA>
90 <NA> <NA>
91 <NA> <NA>
92 <NA> <NA>
93 <NA> <NA>
94 <NA> <NA>
95 <NA> <NA>
96 <NA> <NA>
97 <NA> <NA>
98 <NA> <NA>
99 <NA> <NA>
100 <NA> <NA>
101 <NA> <NA>
102 <NA> <NA>
103 <NA> <NA>
104 <NA> <NA>
105 <NA> <NA>
106 <NA> <NA>
107 <NA> <NA>
108 <NA> <NA>
109 <NA> <NA>
110 <NA> <NA>
111 <NA> <NA>
112 <NA> <NA>
113 <NA> <NA>
114 <NA> <NA>
115 <NA> <NA>
116 <NA> <NA>
117 <NA> <NA>
118 <NA> <NA>
119 <NA> <NA>
120 <NA> <NA>
121 <NA> <NA>
122 <NA> <NA>
123 <NA> <NA>
124 <NA> <NA>
125 <NA> <NA>
126 <NA> <NA>
127 <NA> <NA>
128 <NA> <NA>
129 <NA> <NA>
130 <NA> <NA>
131 <NA> <NA>
132 <NA> <NA>
133 <NA> <NA>
134 <NA> <NA>
135 <NA> <NA>
136 <NA> <NA>
137 <NA> <NA>
138 <NA> <NA>
139 <NA> <NA>
140 <NA> <NA>
141 <NA> <NA>
142 <NA> <NA>
143 <NA> <NA>
144 <NA> <NA>
145 <NA> <NA>
146 <NA> <NA>
147 <NA> <NA>
148 <NA> <NA>
149 <NA> <NA>
150 <NA> <NA>
151 <NA> <NA>
DataQuality.LowerConfidenceLimitValue
1 <NA>
2 <NA>
3 <NA>
4 <NA>
5 <NA>
6 <NA>
7 <NA>
8 <NA>
9 <NA>
10 <NA>
11 <NA>
12 <NA>
13 <NA>
14 <NA>
15 <NA>
16 <NA>
17 <NA>
18 <NA>
19 <NA>
20 <NA>
21 <NA>
22 <NA>
23 <NA>
24 <NA>
25 <NA>
26 <NA>
27 <NA>
28 <NA>
29 <NA>
30 <NA>
31 <NA>
32 <NA>
33 <NA>
34 <NA>
35 <NA>
36 <NA>
37 <NA>
38 <NA>
39 <NA>
40 <NA>
41 <NA>
42 <NA>
43 <NA>
44 <NA>
45 <NA>
46 <NA>
47 <NA>
48 <NA>
49 <NA>
50 <NA>
51 <NA>
52 <NA>
53 <NA>
54 <NA>
55 <NA>
56 <NA>
57 <NA>
58 <NA>
59 <NA>
60 <NA>
61 <NA>
62 <NA>
63 <NA>
64 <NA>
65 <NA>
66 <NA>
67 <NA>
68 <NA>
69 <NA>
70 <NA>
71 <NA>
72 <NA>
73 <NA>
74 <NA>
75 <NA>
76 <NA>
77 <NA>
78 <NA>
79 <NA>
80 <NA>
81 <NA>
82 <NA>
83 <NA>
84 <NA>
85 <NA>
86 <NA>
87 <NA>
88 <NA>
89 <NA>
90 <NA>
91 <NA>
92 <NA>
93 <NA>
94 <NA>
95 <NA>
96 <NA>
97 <NA>
98 <NA>
99 <NA>
100 <NA>
101 <NA>
102 <NA>
103 <NA>
104 <NA>
105 <NA>
106 <NA>
107 <NA>
108 <NA>
109 <NA>
110 <NA>
111 <NA>
112 <NA>
113 <NA>
114 <NA>
115 <NA>
116 <NA>
117 <NA>
118 <NA>
119 <NA>
120 <NA>
121 <NA>
122 <NA>
123 <NA>
124 <NA>
125 <NA>
126 <NA>
127 <NA>
128 <NA>
129 <NA>
130 <NA>
131 <NA>
132 <NA>
133 <NA>
134 <NA>
135 <NA>
136 <NA>
137 <NA>
138 <NA>
139 <NA>
140 <NA>
141 <NA>
142 <NA>
143 <NA>
144 <NA>
145 <NA>
146 <NA>
147 <NA>
148 <NA>
149 <NA>
150 <NA>
151 <NA>
ResultCommentText
1 <NA>
2 <NA>
3 <NA>
4 <NA>
5 <NA>
6 <NA>
7 <NA>
8 <NA>
9 <NA>
10 <NA>
11 <NA>
12 <NA>
13 <NA>
14 <NA>
15 <NA>
16 <NA>
17 <NA>
18 <NA>
19 <NA>
20 <NA>
21 <NA>
22 <NA>
23 <NA>
24 <NA>
25 <NA>
26 <NA>
27 <NA>
28 <NA>
29 <NA>
30 <NA>
31 <NA>
32 <NA>
33 <NA>
34 <NA>
35 <NA>
36 <NA>
37 <NA>
38 <NA>
39 <NA>
40 <NA>
41 <NA>
42 <NA>
43 <NA>
44 <NA>
45 <NA>
46 <NA>
47 <NA>
48 <NA>
49 <NA>
50 <NA>
51 <NA>
52 <NA>
53 <NA>
54 <NA>
55 <NA>
56 <NA>
57 <NA>
58 Report level code updated Oct., Nov. 2015. Reference: NWQL TM 2015.02 (RLC: LT-MDL => DLDQC)
59 <NA>
60 <NA>
61 <NA>
62 <NA>
63 The parameter 00665 was swapped from labcode 2333 to labcode 1984 because the result from labcode 2333 exceeded the calibration range.
64 <NA>
65 <NA>
66 <NA>
67 <NA>
68 <NA>
69 <NA>
70 Report level code updated Oct., Nov. 2015. Reference: NWQL TM 2015.02 (RLC: LT-MDL => DLDQC)
71 The parameter 00665 was swapped from labcode 2333 to labcode 2759 because the result from labcode 2333 exceeded the calibration range.
72 <NA>
73 <NA>
74 <NA>
75 <NA>
76 <NA>
77 The parameter 00665 was swapped from labcode 2333 to labcode 2759
78 <NA>
79 <NA>
80 Report level code updated Oct., Nov. 2015. Reference: NWQL TM 2015.02 (RLC: LT-MDL => DLDQC)
81 Report level code updated Oct., Nov. 2015. Reference: NWQL TM 2015.02 (RLC: LT-MDL => DLDQC)
82 Report level code updated Oct., Nov. 2015. Reference: NWQL TM 2015.02 (RLC: LT-MDL => DLDQC)
83 Report level code updated Oct., Nov. 2015. Reference: NWQL TM 2015.02 (RLC: LT-MDL => DLDQC)
84 Report level code updated Oct., Nov. 2015. Reference: NWQL TM 2015.02 (RLC: LT-MDL => DLDQC)
85 Report level code updated Oct., Nov. 2015. Reference: NWQL TM 2015.02 (RLC: LT-MDL => DLDQC)
86 <NA>
87 <NA>
88 Report level code updated Oct., Nov. 2015. Reference: NWQL TM 2015.02 (RLC: LT-MDL => DLDQC)
89 <NA>
90 Report level code updated Oct., Nov. 2015. Reference: NWQL TM 2015.02 (RLC: LT-MDL => DLDQC)
91 <NA>
92 <NA>
93 <NA>
94 Report level code updated Oct., Nov. 2015. Reference: NWQL TM 2015.02 (RLC: LT-MDL => DLDQC)
95 <NA>
96 Report level code updated Oct., Nov. 2015. Reference: NWQL TM 2015.02 (RLC: LT-MDL => DLDQC)
97 Report level code updated Oct., Nov. 2015. Reference: NWQL TM 2015.02 (RLC: LT-MDL => DLDQC)
98 <NA>
99 <NA>
100 <NA>
101 <NA>
102 <NA>
103 <NA>
104 <NA>
105 <NA>
106 <NA>
107 <NA>
108 <NA>
109 <NA>
110 <NA>
111 <NA>
112 <NA>
113 <NA>
114 <NA>
115 <NA>
116 <NA>
117 <NA>
118 <NA>
119 <NA>
120 <NA>
121 <NA>
122 <NA>
123 <NA>
124 <NA>
125 <NA>
126 <NA>
127 <NA>
128 <NA>
129 <NA>
130 <NA>
131 <NA>
132 <NA>
133 <NA>
134 <NA>
135 <NA>
136 <NA>
137 <NA>
138 <NA>
139 <NA>
140 <NA>
141 <NA>
142 <NA>
143 <NA>
144 <NA>
145 <NA>
146 <NA>
147 <NA>
148 <NA>
149 <NA>
150 <NA>
151 <NA>
USGSPCode ResultDepthHeightMeasure.MeasureValue
1 00665 NA
2 00666 NA
3 00666 NA
4 00665 NA
5 00666 NA
6 00666 NA
7 00665 NA
8 00666 NA
9 00665 NA
10 00666 NA
11 00665 NA
12 00666 NA
13 00666 NA
14 00665 NA
15 00665 NA
16 00666 NA
17 00665 NA
18 00666 NA
19 00665 NA
20 00666 NA
21 00665 NA
22 00666 NA
23 00665 NA
24 00666 NA
25 00665 NA
26 00666 NA
27 00665 NA
28 00666 NA
29 00665 NA
30 00666 NA
31 00665 NA
32 00666 NA
33 00665 NA
34 00666 NA
35 00665 NA
36 00666 NA
37 00665 NA
38 00665 NA
39 00665 NA
40 00666 NA
41 00665 NA
42 00666 NA
43 00665 NA
44 00666 NA
45 00665 NA
46 00666 NA
47 00666 NA
48 00665 NA
49 00666 NA
50 00665 NA
51 00665 NA
52 00666 NA
53 00666 NA
54 00665 NA
55 00666 NA
56 00665 NA
57 00665 NA
58 00665 NA
59 00665 NA
60 00666 NA
61 00665 NA
62 00665 NA
63 00665 NA
64 00665 NA
65 00665 NA
66 00665 NA
67 00665 NA
68 00665 NA
69 00665 NA
70 00665 NA
71 00665 NA
72 00665 NA
73 00665 NA
74 00666 NA
75 00665 NA
76 00665 NA
77 00665 NA
78 00665 NA
79 00665 NA
80 00665 NA
81 00665 NA
82 00665 NA
83 00665 NA
84 00665 NA
85 00665 NA
86 00665 NA
87 00665 NA
88 00665 NA
89 00665 NA
90 00665 NA
91 00666 NA
92 00665 NA
93 00665 NA
94 00665 NA
95 00665 NA
96 00665 NA
97 00665 NA
98 00666 NA
99 00665 NA
100 00665 NA
101 00666 NA
102 00665 NA
103 00666 NA
104 00666 NA
105 00666 NA
106 00665 NA
107 00665 NA
108 00666 NA
109 00665 NA
110 00666 NA
111 00666 NA
112 00666 NA
113 00665 NA
114 00665 NA
115 00665 NA
116 00666 NA
117 00665 NA
118 00666 NA
119 00665 NA
120 00666 NA
121 00665 NA
122 00666 NA
123 00666 NA
124 00665 NA
125 00666 NA
126 00665 NA
127 00666 NA
128 00666 NA
129 00665 NA
130 00666 NA
131 00665 NA
132 00665 NA
133 00666 NA
134 00665 NA
135 00665 NA
136 00666 NA
137 00665 NA
138 00666 NA
139 00666 NA
140 00665 NA
141 00665 NA
142 00666 NA
143 00665 NA
144 00666 NA
145 00666 NA
146 00665 NA
147 00666 NA
148 00665 NA
149 00665 NA
150 00666 NA
151 00665 NA
ResultDepthHeightMeasure.MeasureUnitCode
1 <NA>
2 <NA>
3 <NA>
4 <NA>
5 <NA>
6 <NA>
7 <NA>
8 <NA>
9 <NA>
10 <NA>
11 <NA>
12 <NA>
13 <NA>
14 <NA>
15 <NA>
16 <NA>
17 <NA>
18 <NA>
19 <NA>
20 <NA>
21 <NA>
22 <NA>
23 <NA>
24 <NA>
25 <NA>
26 <NA>
27 <NA>
28 <NA>
29 <NA>
30 <NA>
31 <NA>
32 <NA>
33 <NA>
34 <NA>
35 <NA>
36 <NA>
37 <NA>
38 <NA>
39 <NA>
40 <NA>
41 <NA>
42 <NA>
43 <NA>
44 <NA>
45 <NA>
46 <NA>
47 <NA>
48 <NA>
49 <NA>
50 <NA>
51 <NA>
52 <NA>
53 <NA>
54 <NA>
55 <NA>
56 <NA>
57 <NA>
58 <NA>
59 <NA>
60 <NA>
61 <NA>
62 <NA>
63 <NA>
64 <NA>
65 <NA>
66 <NA>
67 <NA>
68 <NA>
69 <NA>
70 <NA>
71 <NA>
72 <NA>
73 <NA>
74 <NA>
75 <NA>
76 <NA>
77 <NA>
78 <NA>
79 <NA>
80 <NA>
81 <NA>
82 <NA>
83 <NA>
84 <NA>
85 <NA>
86 <NA>
87 <NA>
88 <NA>
89 <NA>
90 <NA>
91 <NA>
92 <NA>
93 <NA>
94 <NA>
95 <NA>
96 <NA>
97 <NA>
98 <NA>
99 <NA>
100 <NA>
101 <NA>
102 <NA>
103 <NA>
104 <NA>
105 <NA>
106 <NA>
107 <NA>
108 <NA>
109 <NA>
110 <NA>
111 <NA>
112 <NA>
113 <NA>
114 <NA>
115 <NA>
116 <NA>
117 <NA>
118 <NA>
119 <NA>
120 <NA>
121 <NA>
122 <NA>
123 <NA>
124 <NA>
125 <NA>
126 <NA>
127 <NA>
128 <NA>
129 <NA>
130 <NA>
131 <NA>
132 <NA>
133 <NA>
134 <NA>
135 <NA>
136 <NA>
137 <NA>
138 <NA>
139 <NA>
140 <NA>
141 <NA>
142 <NA>
143 <NA>
144 <NA>
145 <NA>
146 <NA>
147 <NA>
148 <NA>
149 <NA>
150 <NA>
151 <NA>
ResultDepthAltitudeReferencePointText SubjectTaxonomicName
1 <NA> <NA>
2 <NA> <NA>
3 <NA> <NA>
4 <NA> <NA>
5 <NA> <NA>
6 <NA> <NA>
7 <NA> <NA>
8 <NA> <NA>
9 <NA> <NA>
10 <NA> <NA>
11 <NA> <NA>
12 <NA> <NA>
13 <NA> <NA>
14 <NA> <NA>
15 <NA> <NA>
16 <NA> <NA>
17 <NA> <NA>
18 <NA> <NA>
19 <NA> <NA>
20 <NA> <NA>
21 <NA> <NA>
22 <NA> <NA>
23 <NA> <NA>
24 <NA> <NA>
25 <NA> <NA>
26 <NA> <NA>
27 <NA> <NA>
28 <NA> <NA>
29 <NA> <NA>
30 <NA> <NA>
31 <NA> <NA>
32 <NA> <NA>
33 <NA> <NA>
34 <NA> <NA>
35 <NA> <NA>
36 <NA> <NA>
37 <NA> <NA>
38 <NA> <NA>
39 <NA> <NA>
40 <NA> <NA>
41 <NA> <NA>
42 <NA> <NA>
43 <NA> <NA>
44 <NA> <NA>
45 <NA> <NA>
46 <NA> <NA>
47 <NA> <NA>
48 <NA> <NA>
49 <NA> <NA>
50 <NA> <NA>
51 <NA> <NA>
52 <NA> <NA>
53 <NA> <NA>
54 <NA> <NA>
55 <NA> <NA>
56 <NA> <NA>
57 <NA> <NA>
58 <NA> <NA>
59 <NA> <NA>
60 <NA> <NA>
61 <NA> <NA>
62 <NA> <NA>
63 <NA> <NA>
64 <NA> <NA>
65 <NA> <NA>
66 <NA> <NA>
67 <NA> <NA>
68 <NA> <NA>
69 <NA> <NA>
70 <NA> <NA>
71 <NA> <NA>
72 <NA> <NA>
73 <NA> <NA>
74 <NA> <NA>
75 <NA> <NA>
76 <NA> <NA>
77 <NA> <NA>
78 <NA> <NA>
79 <NA> <NA>
80 <NA> <NA>
81 <NA> <NA>
82 <NA> <NA>
83 <NA> <NA>
84 <NA> <NA>
85 <NA> <NA>
86 <NA> <NA>
87 <NA> <NA>
88 <NA> <NA>
89 <NA> <NA>
90 <NA> <NA>
91 <NA> <NA>
92 <NA> <NA>
93 <NA> <NA>
94 <NA> <NA>
95 <NA> <NA>
96 <NA> <NA>
97 <NA> <NA>
98 <NA> <NA>
99 <NA> <NA>
100 <NA> <NA>
101 <NA> <NA>
102 <NA> <NA>
103 <NA> <NA>
104 <NA> <NA>
105 <NA> <NA>
106 <NA> <NA>
107 <NA> <NA>
108 <NA> <NA>
109 <NA> <NA>
110 <NA> <NA>
111 <NA> <NA>
112 <NA> <NA>
113 <NA> <NA>
114 <NA> <NA>
115 <NA> <NA>
116 <NA> <NA>
117 <NA> <NA>
118 <NA> <NA>
119 <NA> <NA>
120 <NA> <NA>
121 <NA> <NA>
122 <NA> <NA>
123 <NA> <NA>
124 <NA> <NA>
125 <NA> <NA>
126 <NA> <NA>
127 <NA> <NA>
128 <NA> <NA>
129 <NA> <NA>
130 <NA> <NA>
131 <NA> <NA>
132 <NA> <NA>
133 <NA> <NA>
134 <NA> <NA>
135 <NA> <NA>
136 <NA> <NA>
137 <NA> <NA>
138 <NA> <NA>
139 <NA> <NA>
140 <NA> <NA>
141 <NA> <NA>
142 <NA> <NA>
143 <NA> <NA>
144 <NA> <NA>
145 <NA> <NA>
146 <NA> <NA>
147 <NA> <NA>
148 <NA> <NA>
149 <NA> <NA>
150 <NA> <NA>
151 <NA> <NA>
SampleTissueAnatomyName BinaryObjectFileName BinaryObjectFileTypeCode
1 <NA> <NA> <NA>
2 <NA> <NA> <NA>
3 <NA> <NA> <NA>
4 <NA> <NA> <NA>
5 <NA> <NA> <NA>
6 <NA> <NA> <NA>
7 <NA> <NA> <NA>
8 <NA> <NA> <NA>
9 <NA> <NA> <NA>
10 <NA> <NA> <NA>
11 <NA> <NA> <NA>
12 <NA> <NA> <NA>
13 <NA> <NA> <NA>
14 <NA> <NA> <NA>
15 <NA> <NA> <NA>
16 <NA> <NA> <NA>
17 <NA> <NA> <NA>
18 <NA> <NA> <NA>
19 <NA> <NA> <NA>
20 <NA> <NA> <NA>
21 <NA> <NA> <NA>
22 <NA> <NA> <NA>
23 <NA> <NA> <NA>
24 <NA> <NA> <NA>
25 <NA> <NA> <NA>
26 <NA> <NA> <NA>
27 <NA> <NA> <NA>
28 <NA> <NA> <NA>
29 <NA> <NA> <NA>
30 <NA> <NA> <NA>
31 <NA> <NA> <NA>
32 <NA> <NA> <NA>
33 <NA> <NA> <NA>
34 <NA> <NA> <NA>
35 <NA> <NA> <NA>
36 <NA> <NA> <NA>
37 <NA> <NA> <NA>
38 <NA> <NA> <NA>
39 <NA> <NA> <NA>
40 <NA> <NA> <NA>
41 <NA> <NA> <NA>
42 <NA> <NA> <NA>
43 <NA> <NA> <NA>
44 <NA> <NA> <NA>
45 <NA> <NA> <NA>
46 <NA> <NA> <NA>
47 <NA> <NA> <NA>
48 <NA> <NA> <NA>
49 <NA> <NA> <NA>
50 <NA> <NA> <NA>
51 <NA> <NA> <NA>
52 <NA> <NA> <NA>
53 <NA> <NA> <NA>
54 <NA> <NA> <NA>
55 <NA> <NA> <NA>
56 <NA> <NA> <NA>
57 <NA> <NA> <NA>
58 <NA> <NA> <NA>
59 <NA> <NA> <NA>
60 <NA> <NA> <NA>
61 <NA> <NA> <NA>
62 <NA> <NA> <NA>
63 <NA> <NA> <NA>
64 <NA> <NA> <NA>
65 <NA> <NA> <NA>
66 <NA> <NA> <NA>
67 <NA> <NA> <NA>
68 <NA> <NA> <NA>
69 <NA> <NA> <NA>
70 <NA> <NA> <NA>
71 <NA> <NA> <NA>
72 <NA> <NA> <NA>
73 <NA> <NA> <NA>
74 <NA> <NA> <NA>
75 <NA> <NA> <NA>
76 <NA> <NA> <NA>
77 <NA> <NA> <NA>
78 <NA> <NA> <NA>
79 <NA> <NA> <NA>
80 <NA> <NA> <NA>
81 <NA> <NA> <NA>
82 <NA> <NA> <NA>
83 <NA> <NA> <NA>
84 <NA> <NA> <NA>
85 <NA> <NA> <NA>
86 <NA> <NA> <NA>
87 <NA> <NA> <NA>
88 <NA> <NA> <NA>
89 <NA> <NA> <NA>
90 <NA> <NA> <NA>
91 <NA> <NA> <NA>
92 <NA> <NA> <NA>
93 <NA> <NA> <NA>
94 <NA> <NA> <NA>
95 <NA> <NA> <NA>
96 <NA> <NA> <NA>
97 <NA> <NA> <NA>
98 <NA> <NA> <NA>
99 <NA> <NA> <NA>
100 <NA> <NA> <NA>
101 <NA> <NA> <NA>
102 <NA> <NA> <NA>
103 <NA> <NA> <NA>
104 <NA> <NA> <NA>
105 <NA> <NA> <NA>
106 <NA> <NA> <NA>
107 <NA> <NA> <NA>
108 <NA> <NA> <NA>
109 <NA> <NA> <NA>
110 <NA> <NA> <NA>
111 <NA> <NA> <NA>
112 <NA> <NA> <NA>
113 <NA> <NA> <NA>
114 <NA> <NA> <NA>
115 <NA> <NA> <NA>
116 <NA> <NA> <NA>
117 <NA> <NA> <NA>
118 <NA> <NA> <NA>
119 <NA> <NA> <NA>
120 <NA> <NA> <NA>
121 <NA> <NA> <NA>
122 <NA> <NA> <NA>
123 <NA> <NA> <NA>
124 <NA> <NA> <NA>
125 <NA> <NA> <NA>
126 <NA> <NA> <NA>
127 <NA> <NA> <NA>
128 <NA> <NA> <NA>
129 <NA> <NA> <NA>
130 <NA> <NA> <NA>
131 <NA> <NA> <NA>
132 <NA> <NA> <NA>
133 <NA> <NA> <NA>
134 <NA> <NA> <NA>
135 <NA> <NA> <NA>
136 <NA> <NA> <NA>
137 <NA> <NA> <NA>
138 <NA> <NA> <NA>
139 <NA> <NA> <NA>
140 <NA> <NA> <NA>
141 <NA> <NA> <NA>
142 <NA> <NA> <NA>
143 <NA> <NA> <NA>
144 <NA> <NA> <NA>
145 <NA> <NA> <NA>
146 <NA> <NA> <NA>
147 <NA> <NA> <NA>
148 <NA> <NA> <NA>
149 <NA> <NA> <NA>
150 <NA> <NA> <NA>
151 <NA> <NA> <NA>
ResultFileUrl ResultAnalyticalMethod.MethodIdentifier
1 <NA> CL084
2 <NA> CL052
3 <NA> CL061
4 <NA> KJ010
5 <NA> CL061
6 <NA> CL061
7 <NA> CL001
8 <NA> CL059
9 <NA> KJ010
10 <NA> CL061
11 <NA> KJ010
12 <NA> CL061
13 <NA> CL020
14 <NA> CL021
15 <NA> KJ010
16 <NA> CL061
17 <NA> KJ010
18 <NA> CL061
19 <NA> CL001
20 <NA> CL059
21 <NA> CL001
22 <NA> CL059
23 <NA> KJ010
24 <NA> CL061
25 <NA> CL001
26 <NA> CL059
27 <NA> KJ010
28 <NA> CL061
29 <NA> CL001
30 <NA> CL059
31 <NA> CL001
32 <NA> CL059
33 <NA> CL001
34 <NA> CL059
35 <NA> CL001
36 <NA> CL059
37 <NA> KJ009
38 <NA> KJ009
39 <NA> CL021
40 <NA> CL020
41 <NA> CL021
42 <NA> CL020
43 <NA> CL021
44 <NA> CL020
45 <NA> KJ009
46 <NA> KJ005
47 <NA> CL020
48 <NA> CL021
49 <NA> CL020
50 <NA> CL021
51 <NA> KJ009
52 <NA> KJ005
53 <NA> KJ005
54 <NA> KJ009
55 <NA> KJ005
56 <NA> CL021
57 <NA> CL021
58 <NA> CL021
59 <NA> KJ009
60 <NA> KJ005
61 <NA> CL021
62 <NA> CL021
63 <NA> KJ009
64 <NA> CL021
65 <NA> KJ009
66 <NA> CL021
67 <NA> CL021
68 <NA> CL021
69 <NA> CL021
70 <NA> CL021
71 <NA> AKP01
72 <NA> CL021
73 <NA> KJ009
74 <NA> KJ005
75 <NA> CL021
76 <NA> CL021
77 <NA> AKP01
78 <NA> CL021
79 <NA> CL021
80 <NA> CL021
81 <NA> CL021
82 <NA> CL021
83 <NA> CL021
84 <NA> CL021
85 <NA> CL021
86 <NA> CL021
87 <NA> CL021
88 <NA> CL021
89 <NA> CL021
90 <NA> CL021
91 <NA> CL020
92 <NA> CL021
93 <NA> CL021
94 <NA> CL021
95 <NA> CL021
96 <NA> CL021
97 <NA> CL021
98 <NA> KJ005
99 <NA> KJ009
100 <NA> KJ009
101 <NA> KJ005
102 <NA> KJ009
103 <NA> KJ005
104 <NA> KJ005
105 <NA> KJ005
106 <NA> KJ009
107 <NA> KJ009
108 <NA> KJ005
109 <NA> KJ009
110 <NA> KJ005
111 <NA> KJ005
112 <NA> KJ005
113 <NA> KJ009
114 <NA> KJ009
115 <NA> KJ009
116 <NA> KJ005
117 <NA> KJ009
118 <NA> KJ005
119 <NA> KJ009
120 <NA> KJ005
121 <NA> KJ009
122 <NA> KJ005
123 <NA> CL020
124 <NA> CL021
125 <NA> CL020
126 <NA> KJ009
127 <NA> KJ005
128 <NA> KJ005
129 <NA> KJ009
130 <NA> KJ005
131 <NA> KJ009
132 <NA> KJ009
133 <NA> KJ005
134 <NA> KJ009
135 <NA> CL021
136 <NA> KJ005
137 <NA> KJ009
138 <NA> KJ005
139 <NA> KJ005
140 <NA> KJ009
141 <NA> KJ009
142 <NA> KJ005
143 <NA> KJ009
144 <NA> KJ005
145 <NA> KJ005
146 <NA> KJ009
147 <NA> KJ005
148 <NA> KJ009
149 <NA> KJ009
150 <NA> KJ005
151 <NA> KJ009
ResultAnalyticalMethod.MethodIdentifierContext
1 USGS
2 USGS
3 USGS
4 USGS
5 USGS
6 USGS
7 USGS
8 USGS
9 USGS
10 USGS
11 USGS
12 USGS
13 USGS
14 USGS
15 USGS
16 USGS
17 USGS
18 USGS
19 USGS
20 USGS
21 USGS
22 USGS
23 USGS
24 USGS
25 USGS
26 USGS
27 USGS
28 USGS
29 USGS
30 USGS
31 USGS
32 USGS
33 USGS
34 USGS
35 USGS
36 USGS
37 USGS
38 USGS
39 USGS
40 USGS
41 USGS
42 USGS
43 USGS
44 USGS
45 USGS
46 USGS
47 USGS
48 USGS
49 USGS
50 USGS
51 USGS
52 USGS
53 USGS
54 USGS
55 USGS
56 USGS
57 USGS
58 USGS
59 USGS
60 USGS
61 USGS
62 USGS
63 USGS
64 USGS
65 USGS
66 USGS
67 USGS
68 USGS
69 USGS
70 USGS
71 USGS
72 USGS
73 USGS
74 USGS
75 USGS
76 USGS
77 USGS
78 USGS
79 USGS
80 USGS
81 USGS
82 USGS
83 USGS
84 USGS
85 USGS
86 USGS
87 USGS
88 USGS
89 USGS
90 USGS
91 USGS
92 USGS
93 USGS
94 USGS
95 USGS
96 USGS
97 USGS
98 USGS
99 USGS
100 USGS
101 USGS
102 USGS
103 USGS
104 USGS
105 USGS
106 USGS
107 USGS
108 USGS
109 USGS
110 USGS
111 USGS
112 USGS
113 USGS
114 USGS
115 USGS
116 USGS
117 USGS
118 USGS
119 USGS
120 USGS
121 USGS
122 USGS
123 USGS
124 USGS
125 USGS
126 USGS
127 USGS
128 USGS
129 USGS
130 USGS
131 USGS
132 USGS
133 USGS
134 USGS
135 USGS
136 USGS
137 USGS
138 USGS
139 USGS
140 USGS
141 USGS
142 USGS
143 USGS
144 USGS
145 USGS
146 USGS
147 USGS
148 USGS
149 USGS
150 USGS
151 USGS
ResultAnalyticalMethod.MethodName ResultAnalyticalMethod.MethodUrl
1 Phosphorus, wu, ASF phosphomolyb <NA>
2 Phosphorus, wf, auto phosphomoly <NA>
3 Phosphorus, wf,microkjeldahl ASF <NA>
4 Phosphorus, wu, microKJ ASF, Hg <NA>
5 Phosphorus, wf,microkjeldahl ASF <NA>
6 Phosphorus, wf,microkjeldahl ASF <NA>
7 ASF, colorimetric <NA>
8 Phosphorus, LIS, ASF phosphomol <NA>
9 Phosphorus, wu, microKJ ASF, Hg <NA>
10 Phosphorus, wf,microkjeldahl ASF <NA>
11 Phosphorus, wu, microKJ ASF, Hg <NA>
12 Phosphorus, wf,microkjeldahl ASF <NA>
13 P, wf, FCC, persulfate CF color <NA>
14 P, wu, WCA, persulfate CF color <NA>
15 Phosphorus, wu, microKJ ASF, Hg <NA>
16 Phosphorus, wf,microkjeldahl ASF <NA>
17 Phosphorus, wu, microKJ ASF, Hg <NA>
18 Phosphorus, wf,microkjeldahl ASF <NA>
19 ASF, colorimetric <NA>
20 Phosphorus, LIS, ASF phosphomol <NA>
21 ASF, colorimetric <NA>
22 Phosphorus, LIS, ASF phosphomol <NA>
23 Phosphorus, wu, microKJ ASF, Hg <NA>
24 Phosphorus, wf,microkjeldahl ASF <NA>
25 ASF, colorimetric <NA>
26 Phosphorus, LIS, ASF phosphomol <NA>
27 Phosphorus, wu, microKJ ASF, Hg <NA>
28 Phosphorus, wf,microkjeldahl ASF <NA>
29 ASF, colorimetric <NA>
30 Phosphorus, LIS, ASF phosphomol <NA>
31 ASF, colorimetric <NA>
32 Phosphorus, LIS, ASF phosphomol <NA>
33 ASF, colorimetric <NA>
34 Phosphorus, LIS, ASF phosphomol <NA>
35 ASF, colorimetric <NA>
36 Phosphorus, LIS, ASF phosphomol <NA>
37 Phosphorus, wu, microKJ ASF, H+ <NA>
38 Phosphorus, wu, microKJ ASF, H+ <NA>
39 P, wu, WCA, persulfate CF color <NA>
40 P, wf, FCC, persulfate CF color <NA>
41 P, wu, WCA, persulfate CF color <NA>
42 P, wf, FCC, persulfate CF color <NA>
43 P, wu, WCA, persulfate CF color <NA>
44 P, wf, FCC, persulfate CF color <NA>
45 Phosphorus, wu, microKJ ASF, H+ <NA>
46 P, wf, FCC, Kjeldahl, CF <NA>
47 P, wf, FCC, persulfate CF color <NA>
48 P, wu, WCA, persulfate CF color <NA>
49 P, wf, FCC, persulfate CF color <NA>
50 P, wu, WCA, persulfate CF color <NA>
51 Phosphorus, wu, microKJ ASF, H+ <NA>
52 P, wf, FCC, Kjeldahl, CF <NA>
53 P, wf, FCC, Kjeldahl, CF <NA>
54 Phosphorus, wu, microKJ ASF, H+ <NA>
55 P, wf, FCC, Kjeldahl, CF <NA>
56 P, wu, WCA, persulfate CF color <NA>
57 P, wu, WCA, persulfate CF color <NA>
58 P, wu, WCA, persulfate CF color <NA>
59 Phosphorus, wu, microKJ ASF, H+ <NA>
60 P, wf, FCC, Kjeldahl, CF <NA>
61 P, wu, WCA, persulfate CF color <NA>
62 P, wu, WCA, persulfate CF color <NA>
63 Phosphorus, wu, microKJ ASF, H+ <NA>
64 P, wu, WCA, persulfate CF color <NA>
65 Phosphorus, wu, microKJ ASF, H+ <NA>
66 P, wu, WCA, persulfate CF color <NA>
67 P, wu, WCA, persulfate CF color <NA>
68 P, wu, WCA, persulfate CF color <NA>
69 P, wu, WCA, persulfate CF color <NA>
70 P, wu, WCA, persulfate CF color <NA>
71 Nutrients, wu, WCA,persulfate,CF <NA>
72 P, wu, WCA, persulfate CF color <NA>
73 Phosphorus, wu, microKJ ASF, H+ <NA>
74 P, wf, FCC, Kjeldahl, CF <NA>
75 P, wu, WCA, persulfate CF color <NA>
76 P, wu, WCA, persulfate CF color <NA>
77 Nutrients, wu, WCA,persulfate,CF <NA>
78 P, wu, WCA, persulfate CF color <NA>
79 P, wu, WCA, persulfate CF color <NA>
80 P, wu, WCA, persulfate CF color <NA>
81 P, wu, WCA, persulfate CF color <NA>
82 P, wu, WCA, persulfate CF color <NA>
83 P, wu, WCA, persulfate CF color <NA>
84 P, wu, WCA, persulfate CF color <NA>
85 P, wu, WCA, persulfate CF color <NA>
86 P, wu, WCA, persulfate CF color <NA>
87 P, wu, WCA, persulfate CF color <NA>
88 P, wu, WCA, persulfate CF color <NA>
89 P, wu, WCA, persulfate CF color <NA>
90 P, wu, WCA, persulfate CF color <NA>
91 P, wf, FCC, persulfate CF color <NA>
92 P, wu, WCA, persulfate CF color <NA>
93 P, wu, WCA, persulfate CF color <NA>
94 P, wu, WCA, persulfate CF color <NA>
95 P, wu, WCA, persulfate CF color <NA>
96 P, wu, WCA, persulfate CF color <NA>
97 P, wu, WCA, persulfate CF color <NA>
98 P, wf, FCC, Kjeldahl, CF <NA>
99 Phosphorus, wu, microKJ ASF, H+ <NA>
100 Phosphorus, wu, microKJ ASF, H+ <NA>
101 P, wf, FCC, Kjeldahl, CF <NA>
102 Phosphorus, wu, microKJ ASF, H+ <NA>
103 P, wf, FCC, Kjeldahl, CF <NA>
104 P, wf, FCC, Kjeldahl, CF <NA>
105 P, wf, FCC, Kjeldahl, CF <NA>
106 Phosphorus, wu, microKJ ASF, H+ <NA>
107 Phosphorus, wu, microKJ ASF, H+ <NA>
108 P, wf, FCC, Kjeldahl, CF <NA>
109 Phosphorus, wu, microKJ ASF, H+ <NA>
110 P, wf, FCC, Kjeldahl, CF <NA>
111 P, wf, FCC, Kjeldahl, CF <NA>
112 P, wf, FCC, Kjeldahl, CF <NA>
113 Phosphorus, wu, microKJ ASF, H+ <NA>
114 Phosphorus, wu, microKJ ASF, H+ <NA>
115 Phosphorus, wu, microKJ ASF, H+ <NA>
116 P, wf, FCC, Kjeldahl, CF <NA>
117 Phosphorus, wu, microKJ ASF, H+ <NA>
118 P, wf, FCC, Kjeldahl, CF <NA>
119 Phosphorus, wu, microKJ ASF, H+ <NA>
120 P, wf, FCC, Kjeldahl, CF <NA>
121 Phosphorus, wu, microKJ ASF, H+ <NA>
122 P, wf, FCC, Kjeldahl, CF <NA>
123 P, wf, FCC, persulfate CF color <NA>
124 P, wu, WCA, persulfate CF color <NA>
125 P, wf, FCC, persulfate CF color <NA>
126 Phosphorus, wu, microKJ ASF, H+ <NA>
127 P, wf, FCC, Kjeldahl, CF <NA>
128 P, wf, FCC, Kjeldahl, CF <NA>
129 Phosphorus, wu, microKJ ASF, H+ <NA>
130 P, wf, FCC, Kjeldahl, CF <NA>
131 Phosphorus, wu, microKJ ASF, H+ <NA>
132 Phosphorus, wu, microKJ ASF, H+ <NA>
133 P, wf, FCC, Kjeldahl, CF <NA>
134 Phosphorus, wu, microKJ ASF, H+ <NA>
135 P, wu, WCA, persulfate CF color <NA>
136 P, wf, FCC, Kjeldahl, CF <NA>
137 Phosphorus, wu, microKJ ASF, H+ <NA>
138 P, wf, FCC, Kjeldahl, CF <NA>
139 P, wf, FCC, Kjeldahl, CF <NA>
140 Phosphorus, wu, microKJ ASF, H+ <NA>
141 Phosphorus, wu, microKJ ASF, H+ <NA>
142 P, wf, FCC, Kjeldahl, CF <NA>
143 Phosphorus, wu, microKJ ASF, H+ <NA>
144 P, wf, FCC, Kjeldahl, CF <NA>
145 P, wf, FCC, Kjeldahl, CF <NA>
146 Phosphorus, wu, microKJ ASF, H+ <NA>
147 P, wf, FCC, Kjeldahl, CF <NA>
148 Phosphorus, wu, microKJ ASF, H+ <NA>
149 Phosphorus, wu, microKJ ASF, H+ <NA>
150 P, wf, FCC, Kjeldahl, CF <NA>
151 Phosphorus, wu, microKJ ASF, H+ <NA>
ResultAnalyticalMethod.MethodDescriptionText
1 USGS TWRI 5-A1/1989, p 367
2 USGS TWRI 5-A1/1979, p 453
3 USGS OF 92-146
4 USGS OF 92-146
5 USGS OF 92-146
6 USGS OF 92-146
7 <NA>
8 USGS OF 93-125, p 175
9 USGS OF 92-146
10 USGS OF 92-146
11 USGS OF 92-146
12 USGS OF 92-146
13 <NA>
14 <NA>
15 USGS OF 92-146
16 USGS OF 92-146
17 USGS OF 92-146
18 USGS OF 92-146
19 <NA>
20 USGS OF 93-125, p 175
21 <NA>
22 USGS OF 93-125, p 175
23 USGS OF 92-146
24 USGS OF 92-146
25 <NA>
26 USGS OF 93-125, p 175
27 USGS OF 92-146
28 USGS OF 92-146
29 <NA>
30 USGS OF 93-125, p 175
31 <NA>
32 USGS OF 93-125, p 175
33 <NA>
34 USGS OF 93-125, p 175
35 <NA>
36 USGS OF 93-125, p 175
37 USGS OF 92-146
38 USGS OF 92-146
39 <NA>
40 <NA>
41 <NA>
42 <NA>
43 <NA>
44 <NA>
45 USGS OF 92-146
46 USGS OF 92-146
47 <NA>
48 <NA>
49 <NA>
50 <NA>
51 USGS OF 92-146
52 USGS OF 92-146
53 USGS OF 92-146
54 USGS OF 92-146
55 USGS OF 92-146
56 <NA>
57 <NA>
58 <NA>
59 USGS OF 92-146
60 USGS OF 92-146
61 <NA>
62 <NA>
63 USGS OF 92-146
64 <NA>
65 USGS OF 92-146
66 <NA>
67 <NA>
68 <NA>
69 <NA>
70 <NA>
71 USGS WRI 03-4174
72 <NA>
73 USGS OF 92-146
74 USGS OF 92-146
75 <NA>
76 <NA>
77 USGS WRI 03-4174
78 <NA>
79 <NA>
80 <NA>
81 <NA>
82 <NA>
83 <NA>
84 <NA>
85 <NA>
86 <NA>
87 <NA>
88 <NA>
89 <NA>
90 <NA>
91 <NA>
92 <NA>
93 <NA>
94 <NA>
95 <NA>
96 <NA>
97 <NA>
98 USGS OF 92-146
99 USGS OF 92-146
100 USGS OF 92-146
101 USGS OF 92-146
102 USGS OF 92-146
103 USGS OF 92-146
104 USGS OF 92-146
105 USGS OF 92-146
106 USGS OF 92-146
107 USGS OF 92-146
108 USGS OF 92-146
109 USGS OF 92-146
110 USGS OF 92-146
111 USGS OF 92-146
112 USGS OF 92-146
113 USGS OF 92-146
114 USGS OF 92-146
115 USGS OF 92-146
116 USGS OF 92-146
117 USGS OF 92-146
118 USGS OF 92-146
119 USGS OF 92-146
120 USGS OF 92-146
121 USGS OF 92-146
122 USGS OF 92-146
123 <NA>
124 <NA>
125 <NA>
126 USGS OF 92-146
127 USGS OF 92-146
128 USGS OF 92-146
129 USGS OF 92-146
130 USGS OF 92-146
131 USGS OF 92-146
132 USGS OF 92-146
133 USGS OF 92-146
134 USGS OF 92-146
135 <NA>
136 USGS OF 92-146
137 USGS OF 92-146
138 USGS OF 92-146
139 USGS OF 92-146
140 USGS OF 92-146
141 USGS OF 92-146
142 USGS OF 92-146
143 USGS OF 92-146
144 USGS OF 92-146
145 USGS OF 92-146
146 USGS OF 92-146
147 USGS OF 92-146
148 USGS OF 92-146
149 USGS OF 92-146
150 USGS OF 92-146
151 USGS OF 92-146
LaboratoryName AnalysisStartDate
1 <NA> <NA>
2 <NA> <NA>
3 <NA> <NA>
4 <NA> <NA>
5 <NA> <NA>
6 <NA> <NA>
7 <NA> <NA>
8 <NA> <NA>
9 <NA> <NA>
10 <NA> <NA>
11 <NA> <NA>
12 <NA> <NA>
13 USGS-National Water Quality Lab, Denver, CO 2012-08-08
14 USGS-National Water Quality Lab, Denver, CO 2012-06-26
15 <NA> <NA>
16 <NA> <NA>
17 <NA> <NA>
18 <NA> <NA>
19 <NA> <NA>
20 <NA> <NA>
21 <NA> <NA>
22 <NA> <NA>
23 <NA> <NA>
24 <NA> <NA>
25 <NA> <NA>
26 <NA> <NA>
27 <NA> <NA>
28 <NA> <NA>
29 <NA> <NA>
30 <NA> <NA>
31 <NA> <NA>
32 <NA> <NA>
33 <NA> <NA>
34 <NA> <NA>
35 <NA> <NA>
36 <NA> <NA>
37 <NA> <NA>
38 <NA> <NA>
39 USGS-National Water Quality Lab, Denver, CO 2007-07-19
40 USGS-National Water Quality Lab, Denver, CO 2007-07-19
41 USGS-National Water Quality Lab, Denver, CO 2009-10-01
42 USGS-National Water Quality Lab, Denver, CO 2012-06-22
43 USGS-National Water Quality Lab, Denver, CO 2012-06-26
44 USGS-National Water Quality Lab, Denver, CO 2012-06-28
45 <NA> <NA>
46 <NA> <NA>
47 USGS-National Water Quality Lab, Denver, CO 2012-11-01
48 USGS-National Water Quality Lab, Denver, CO 2012-11-01
49 USGS-National Water Quality Lab, Denver, CO 2012-11-01
50 USGS-National Water Quality Lab, Denver, CO 2012-10-03
51 <NA> <NA>
52 <NA> <NA>
53 <NA> <NA>
54 <NA> <NA>
55 <NA> <NA>
56 USGS-National Water Quality Lab, Denver, CO 2005-06-02
57 USGS-National Water Quality Lab, Denver, CO 2005-05-26
58 USGS-National Water Quality Lab, Denver, CO 2015-06-10
59 <NA> <NA>
60 <NA> <NA>
61 USGS-National Water Quality Lab, Denver, CO 2004-05-28
62 USGS-National Water Quality Lab, Denver, CO 2004-05-28
63 USGS-National Water Quality Lab, Denver, CO 2004-06-01
64 USGS-National Water Quality Lab, Denver, CO 2004-09-09
65 <NA> <NA>
66 USGS-National Water Quality Lab, Denver, CO 2012-08-02
67 USGS-National Water Quality Lab, Denver, CO 2004-05-28
68 USGS-National Water Quality Lab, Denver, CO 2005-05-26
69 USGS-National Water Quality Lab, Denver, CO 2005-06-13
70 USGS-National Water Quality Lab, Denver, CO 2015-07-01
71 USGS-National Water Quality Lab, Denver, CO 2005-07-06
72 USGS-National Water Quality Lab, Denver, CO 2005-06-13
73 <NA> <NA>
74 <NA> <NA>
75 USGS-National Water Quality Lab, Denver, CO 2006-05-03
76 USGS-National Water Quality Lab, Denver, CO 2006-05-03
77 USGS-National Water Quality Lab, Denver, CO 2006-06-26
78 USGS-National Water Quality Lab, Denver, CO 2006-05-11
79 USGS-National Water Quality Lab, Denver, CO 2004-05-28
80 USGS-National Water Quality Lab, Denver, CO 2015-06-10
81 USGS-National Water Quality Lab, Denver, CO 2015-07-07
82 USGS-National Water Quality Lab, Denver, CO 2015-05-28
83 USGS-National Water Quality Lab, Denver, CO 2015-05-11
84 USGS-National Water Quality Lab, Denver, CO 2015-06-23
85 USGS-National Water Quality Lab, Denver, CO 2015-07-01
86 USGS-National Water Quality Lab, Denver, CO 2012-11-01
87 USGS-National Water Quality Lab, Denver, CO 2004-06-24
88 USGS-National Water Quality Lab, Denver, CO 2015-07-07
89 USGS-National Water Quality Lab, Denver, CO 2015-04-30
90 USGS-National Water Quality Lab, Denver, CO 2015-06-04
91 USGS-National Water Quality Lab, Denver, CO 2012-10-01
92 USGS-National Water Quality Lab, Denver, CO 2006-05-31
93 USGS-National Water Quality Lab, Denver, CO 2006-06-09
94 USGS-National Water Quality Lab, Denver, CO 2015-06-23
95 USGS-National Water Quality Lab, Denver, CO 2004-05-28
96 USGS-National Water Quality Lab, Denver, CO 2015-05-20
97 USGS-National Water Quality Lab, Denver, CO 2015-05-13
98 <NA> <NA>
99 <NA> <NA>
100 <NA> <NA>
101 <NA> <NA>
102 <NA> <NA>
103 <NA> <NA>
104 <NA> <NA>
105 <NA> <NA>
106 <NA> <NA>
107 <NA> <NA>
108 <NA> <NA>
109 <NA> <NA>
110 <NA> <NA>
111 <NA> <NA>
112 <NA> <NA>
113 <NA> <NA>
114 <NA> <NA>
115 <NA> <NA>
116 <NA> <NA>
117 <NA> <NA>
118 <NA> <NA>
119 <NA> <NA>
120 <NA> <NA>
121 <NA> <NA>
122 <NA> <NA>
123 USGS-National Water Quality Lab, Denver, CO 2012-01-25
124 USGS-National Water Quality Lab, Denver, CO 2012-01-19
125 USGS-National Water Quality Lab, Denver, CO 2012-01-25
126 <NA> <NA>
127 <NA> <NA>
128 <NA> <NA>
129 <NA> <NA>
130 <NA> <NA>
131 <NA> <NA>
132 <NA> <NA>
133 <NA> <NA>
134 <NA> <NA>
135 USGS-National Water Quality Lab, Denver, CO 2012-01-19
136 <NA> <NA>
137 <NA> <NA>
138 <NA> <NA>
139 <NA> <NA>
140 <NA> <NA>
141 <NA> <NA>
142 <NA> <NA>
143 <NA> <NA>
144 <NA> <NA>
145 <NA> <NA>
146 <NA> <NA>
147 <NA> <NA>
148 <NA> <NA>
149 <NA> <NA>
150 <NA> <NA>
151 <NA> <NA>
ResultLaboratoryCommentText
1 <NA>
2 <NA>
3 <NA>
4 <NA>
5 <NA>
6 <NA>
7 <NA>
8 <NA>
9 <NA>
10 <NA>
11 <NA>
12 <NA>
13 <NA>
14 <NA>
15 <NA>
16 <NA>
17 <NA>
18 <NA>
19 <NA>
20 <NA>
21 <NA>
22 <NA>
23 <NA>
24 <NA>
25 <NA>
26 <NA>
27 <NA>
28 <NA>
29 <NA>
30 <NA>
31 <NA>
32 <NA>
33 <NA>
34 <NA>
35 <NA>
36 <NA>
37 <NA>
38 <NA>
39 <NA>
40 <NA>
41 sample was diluted
42 <NA>
43 <NA>
44 <NA>
45 <NA>
46 <NA>
47 <NA>
48 <NA>
49 <NA>
50 <NA>
51 <NA>
52 <NA>
53 <NA>
54 <NA>
55 <NA>
56 <NA>
57 <NA>
58 <NA>
59 <NA>
60 <NA>
61 <NA>
62 <NA>
63 result determined by alternate methodsee result laboratory comment
64 <NA>
65 <NA>
66 <NA>
67 <NA>
68 <NA>
69 <NA>
70 <NA>
71 holding time exceededresult determined by alternate methodsee result laboratory comment
72 <NA>
73 <NA>
74 <NA>
75 <NA>
76 <NA>
77 result determined by alternate methodsee result laboratory comment
78 <NA>
79 <NA>
80 <NA>
81 <NA>
82 <NA>
83 <NA>
84 <NA>
85 <NA>
86 <NA>
87 <NA>
88 <NA>
89 <NA>
90 <NA>
91 <NA>
92 <NA>
93 <NA>
94 <NA>
95 <NA>
96 <NA>
97 <NA>
98 <NA>
99 <NA>
100 <NA>
101 <NA>
102 <NA>
103 <NA>
104 <NA>
105 <NA>
106 <NA>
107 <NA>
108 <NA>
109 <NA>
110 <NA>
111 <NA>
112 <NA>
113 <NA>
114 <NA>
115 <NA>
116 <NA>
117 <NA>
118 <NA>
119 <NA>
120 <NA>
121 <NA>
122 <NA>
123 <NA>
124 <NA>
125 <NA>
126 <NA>
127 <NA>
128 <NA>
129 <NA>
130 <NA>
131 <NA>
132 <NA>
133 <NA>
134 <NA>
135 <NA>
136 <NA>
137 <NA>
138 <NA>
139 <NA>
140 <NA>
141 <NA>
142 <NA>
143 <NA>
144 <NA>
145 <NA>
146 <NA>
147 <NA>
148 <NA>
149 <NA>
150 <NA>
151 <NA>
ResultDetectionQuantitationLimitUrl DetectionQuantitationLimitTypeName
1 <NA> <NA>
2 <NA> <NA>
3 <NA> <NA>
4 <NA> <NA>
5 <NA> <NA>
6 <NA> <NA>
7 <NA> <NA>
8 <NA> <NA>
9 <NA> <NA>
10 <NA> <NA>
11 <NA> <NA>
12 <NA> <NA>
13 <NA> Long Term Method Detection Level
14 <NA> Long Term Method Detection Level
15 <NA> <NA>
16 <NA> <NA>
17 <NA> <NA>
18 <NA> <NA>
19 <NA> <NA>
20 <NA> <NA>
21 <NA> <NA>
22 <NA> <NA>
23 <NA> <NA>
24 <NA> <NA>
25 <NA> <NA>
26 <NA> <NA>
27 <NA> <NA>
28 <NA> <NA>
29 <NA> <NA>
30 <NA> <NA>
31 <NA> <NA>
32 <NA> <NA>
33 <NA> <NA>
34 <NA> <NA>
35 <NA> <NA>
36 <NA> <NA>
37 <NA> <NA>
38 <NA> <NA>
39 <NA> Laboratory Reporting Level
40 <NA> Laboratory Reporting Level
41 <NA> Laboratory Reporting Level
42 <NA> Long Term Method Detection Level
43 <NA> Long Term Method Detection Level
44 <NA> Long Term Method Detection Level
45 <NA> <NA>
46 <NA> <NA>
47 <NA> Long Term Method Detection Level
48 <NA> Long Term Method Detection Level
49 <NA> Long Term Method Detection Level
50 <NA> Long Term Method Detection Level
51 <NA> <NA>
52 <NA> <NA>
53 <NA> Historical Lower Reporting Limit
54 <NA> <NA>
55 <NA> Historical Lower Reporting Limit
56 <NA> Laboratory Reporting Level
57 <NA> Laboratory Reporting Level
58 <NA> Detection limit by DQCALC
59 <NA> <NA>
60 <NA> Historical Lower Reporting Limit
61 <NA> Laboratory Reporting Level
62 <NA> Laboratory Reporting Level
63 <NA> Laboratory Reporting Level
64 <NA> Laboratory Reporting Level
65 <NA> Historical Lower Reporting Limit
66 <NA> Long Term Method Detection Level
67 <NA> Laboratory Reporting Level
68 <NA> Laboratory Reporting Level
69 <NA> Laboratory Reporting Level
70 <NA> Detection limit by DQCALC
71 <NA> Laboratory Reporting Level
72 <NA> Laboratory Reporting Level
73 <NA> Historical Lower Reporting Limit
74 <NA> Historical Lower Reporting Limit
75 <NA> Laboratory Reporting Level
76 <NA> Laboratory Reporting Level
77 <NA> Laboratory Reporting Level
78 <NA> Laboratory Reporting Level
79 <NA> Laboratory Reporting Level
80 <NA> Detection limit by DQCALC
81 <NA> Detection limit by DQCALC
82 <NA> Detection limit by DQCALC
83 <NA> Detection limit by DQCALC
84 <NA> Detection limit by DQCALC
85 <NA> Detection limit by DQCALC
86 <NA> Long Term Method Detection Level
87 <NA> Laboratory Reporting Level
88 <NA> Detection limit by DQCALC
89 <NA> Detection limit by DQCALC
90 <NA> Detection limit by DQCALC
91 <NA> Long Term Method Detection Level
92 <NA> Laboratory Reporting Level
93 <NA> Laboratory Reporting Level
94 <NA> Detection limit by DQCALC
95 <NA> Laboratory Reporting Level
96 <NA> Detection limit by DQCALC
97 <NA> Detection limit by DQCALC
98 <NA> <NA>
99 <NA> <NA>
100 <NA> <NA>
101 <NA> <NA>
102 <NA> <NA>
103 <NA> <NA>
104 <NA> Historical Lower Reporting Limit
105 <NA> Historical Lower Reporting Limit
106 <NA> <NA>
107 <NA> <NA>
108 <NA> <NA>
109 <NA> <NA>
110 <NA> Historical Lower Reporting Limit
111 <NA> <NA>
112 <NA> Historical Lower Reporting Limit
113 <NA> <NA>
114 <NA> <NA>
115 <NA> <NA>
116 <NA> <NA>
117 <NA> <NA>
118 <NA> <NA>
119 <NA> <NA>
120 <NA> Historical Lower Reporting Limit
121 <NA> Historical Lower Reporting Limit
122 <NA> Historical Lower Reporting Limit
123 <NA> Long Term Method Detection Level
124 <NA> Long Term Method Detection Level
125 <NA> Long Term Method Detection Level
126 <NA> <NA>
127 <NA> <NA>
128 <NA> <NA>
129 <NA> <NA>
130 <NA> Historical Lower Reporting Limit
131 <NA> <NA>
132 <NA> <NA>
133 <NA> <NA>
134 <NA> <NA>
135 <NA> Long Term Method Detection Level
136 <NA> Historical Lower Reporting Limit
137 <NA> <NA>
138 <NA> <NA>
139 <NA> <NA>
140 <NA> <NA>
141 <NA> <NA>
142 <NA> <NA>
143 <NA> <NA>
144 <NA> Historical Lower Reporting Limit
145 <NA> <NA>
146 <NA> <NA>
147 <NA> Historical Lower Reporting Limit
148 <NA> <NA>
149 <NA> <NA>
150 <NA> Historical Lower Reporting Limit
151 <NA> <NA>
DetectionQuantitationLimitMeasure.MeasureValue
1 NA
2 NA
3 NA
4 NA
5 NA
6 NA
7 NA
8 NA
9 NA
10 NA
11 NA
12 NA
13 0.003
14 0.004
15 NA
16 NA
17 NA
18 NA
19 NA
20 NA
21 NA
22 NA
23 NA
24 NA
25 NA
26 NA
27 NA
28 NA
29 NA
30 NA
31 NA
32 NA
33 NA
34 NA
35 NA
36 NA
37 NA
38 NA
39 0.008
40 0.006
41 0.008
42 0.003
43 0.004
44 0.003
45 NA
46 NA
47 0.003
48 0.004
49 0.003
50 0.004
51 NA
52 NA
53 0.010
54 NA
55 0.010
56 0.004
57 0.004
58 0.004
59 NA
60 0.010
61 0.004
62 0.004
63 0.040
64 0.004
65 0.010
66 0.004
67 0.004
68 0.004
69 0.004
70 0.004
71 0.020
72 0.004
73 0.010
74 0.010
75 0.004
76 0.004
77 0.020
78 0.004
79 0.004
80 0.004
81 0.004
82 0.004
83 0.004
84 0.004
85 0.004
86 0.004
87 0.004
88 0.004
89 0.004
90 0.004
91 0.003
92 0.004
93 0.004
94 0.004
95 0.004
96 0.004
97 0.004
98 NA
99 NA
100 NA
101 NA
102 NA
103 NA
104 0.050
105 0.010
106 NA
107 NA
108 NA
109 NA
110 0.010
111 NA
112 0.050
113 NA
114 NA
115 NA
116 NA
117 NA
118 NA
119 NA
120 0.010
121 0.050
122 0.050
123 0.003
124 0.004
125 0.003
126 NA
127 NA
128 NA
129 NA
130 0.010
131 NA
132 NA
133 NA
134 NA
135 0.004
136 0.010
137 NA
138 NA
139 NA
140 NA
141 NA
142 NA
143 NA
144 0.010
145 NA
146 NA
147 0.010
148 NA
149 NA
150 0.050
151 NA
DetectionQuantitationLimitMeasure.MeasureUnitCode LabSamplePreparationUrl
1 <NA> <NA>
2 <NA> <NA>
3 <NA> <NA>
4 <NA> <NA>
5 <NA> <NA>
6 <NA> <NA>
7 <NA> <NA>
8 <NA> <NA>
9 <NA> <NA>
10 <NA> <NA>
11 <NA> <NA>
12 <NA> <NA>
13 mg/l as P <NA>
14 mg/l as P <NA>
15 <NA> <NA>
16 <NA> <NA>
17 <NA> <NA>
18 <NA> <NA>
19 <NA> <NA>
20 <NA> <NA>
21 <NA> <NA>
22 <NA> <NA>
23 <NA> <NA>
24 <NA> <NA>
25 <NA> <NA>
26 <NA> <NA>
27 <NA> <NA>
28 <NA> <NA>
29 <NA> <NA>
30 <NA> <NA>
31 <NA> <NA>
32 <NA> <NA>
33 <NA> <NA>
34 <NA> <NA>
35 <NA> <NA>
36 <NA> <NA>
37 <NA> <NA>
38 <NA> <NA>
39 mg/l as P <NA>
40 mg/l as P <NA>
41 mg/l as P <NA>
42 mg/l as P <NA>
43 mg/l as P <NA>
44 mg/l as P <NA>
45 <NA> <NA>
46 <NA> <NA>
47 mg/l as P <NA>
48 mg/l as P <NA>
49 mg/l as P <NA>
50 mg/l as P <NA>
51 <NA> <NA>
52 <NA> <NA>
53 mg/l as P <NA>
54 <NA> <NA>
55 mg/l as P <NA>
56 mg/l as P <NA>
57 mg/l as P <NA>
58 mg/l as P <NA>
59 <NA> <NA>
60 mg/l as P <NA>
61 mg/l as P <NA>
62 mg/l as P <NA>
63 mg/l as P <NA>
64 mg/l as P <NA>
65 mg/l as P <NA>
66 mg/l as P <NA>
67 mg/l as P <NA>
68 mg/l as P <NA>
69 mg/l as P <NA>
70 mg/l as P <NA>
71 mg/l as P <NA>
72 mg/l as P <NA>
73 mg/l as P <NA>
74 mg/l as P <NA>
75 mg/l as P <NA>
76 mg/l as P <NA>
77 mg/l as P <NA>
78 mg/l as P <NA>
79 mg/l as P <NA>
80 mg/l as P <NA>
81 mg/l as P <NA>
82 mg/l as P <NA>
83 mg/l as P <NA>
84 mg/l as P <NA>
85 mg/l as P <NA>
86 mg/l as P <NA>
87 mg/l as P <NA>
88 mg/l as P <NA>
89 mg/l as P <NA>
90 mg/l as P <NA>
91 mg/l as P <NA>
92 mg/l as P <NA>
93 mg/l as P <NA>
94 mg/l as P <NA>
95 mg/l as P <NA>
96 mg/l as P <NA>
97 mg/l as P <NA>
98 <NA> <NA>
99 <NA> <NA>
100 <NA> <NA>
101 <NA> <NA>
102 <NA> <NA>
103 <NA> <NA>
104 mg/l as P <NA>
105 mg/l as P <NA>
106 <NA> <NA>
107 <NA> <NA>
108 <NA> <NA>
109 <NA> <NA>
110 mg/l as P <NA>
111 <NA> <NA>
112 mg/l as P <NA>
113 <NA> <NA>
114 <NA> <NA>
115 <NA> <NA>
116 <NA> <NA>
117 <NA> <NA>
118 <NA> <NA>
119 <NA> <NA>
120 mg/l as P <NA>
121 mg/l as P <NA>
122 mg/l as P <NA>
123 mg/l as P <NA>
124 mg/l as P <NA>
125 mg/l as P <NA>
126 <NA> <NA>
127 <NA> <NA>
128 <NA> <NA>
129 <NA> <NA>
130 mg/l as P <NA>
131 <NA> <NA>
132 <NA> <NA>
133 <NA> <NA>
134 <NA> <NA>
135 mg/l as P <NA>
136 mg/l as P <NA>
137 <NA> <NA>
138 <NA> <NA>
139 <NA> <NA>
140 <NA> <NA>
141 <NA> <NA>
142 <NA> <NA>
143 <NA> <NA>
144 mg/l as P <NA>
145 <NA> <NA>
146 <NA> <NA>
147 mg/l as P <NA>
148 <NA> <NA>
149 <NA> <NA>
150 mg/l as P <NA>
151 <NA> <NA>
LastUpdated ProviderName timeZoneStart timeZoneEnd ActivityStartDateTime
1 <NA> NWIS 7 NA 1991-08-19 23:11:00
2 <NA> NWIS 7 NA 1991-08-19 23:11:00
3 <NA> NWIS 7 NA 1993-07-27 20:00:00
4 <NA> NWIS 7 NA 1993-08-24 23:15:00
5 <NA> NWIS 7 NA 1993-08-24 23:15:00
6 <NA> NWIS 7 NA 1993-07-22 00:00:00
7 <NA> NWIS 7 NA 1994-08-25 00:00:00
8 <NA> NWIS 7 NA 1994-08-25 00:00:00
9 <NA> NWIS 7 NA 1994-07-19 23:10:00
10 <NA> NWIS 7 NA 1994-07-19 23:10:00
11 <NA> NWIS 7 NA 1994-05-20 17:40:00
12 <NA> NWIS 7 NA 1994-05-20 17:40:00
13 <NA> NWIS 7 NA 2012-07-18 17:00:00
14 <NA> NWIS 7 NA 2012-05-17 17:00:00
15 <NA> NWIS 7 NA 1994-05-04 19:10:00
16 <NA> NWIS 7 NA 1994-05-04 19:10:00
17 <NA> NWIS 7 NA 1994-05-19 16:50:00
18 <NA> NWIS 7 NA 1994-05-19 16:50:00
19 <NA> NWIS 7 NA 1994-08-26 00:40:00
20 <NA> NWIS 7 NA 1994-08-26 00:40:00
21 <NA> NWIS 7 NA 1994-08-24 20:40:00
22 <NA> NWIS 7 NA 1994-08-24 20:40:00
23 <NA> NWIS 7 NA 1994-05-18 20:50:00
24 <NA> NWIS 7 NA 1994-05-18 20:50:00
25 <NA> NWIS 7 NA 1994-08-25 01:20:00
26 <NA> NWIS 7 NA 1994-08-25 01:20:00
27 <NA> NWIS 7 NA 1994-08-01 18:30:00
28 <NA> NWIS 7 NA 1994-08-01 18:30:00
29 <NA> NWIS 7 NA 1994-08-25 16:00:00
30 <NA> NWIS 7 NA 1994-08-25 16:00:00
31 <NA> NWIS 7 NA 1994-08-24 22:30:00
32 <NA> NWIS 7 NA 1994-08-24 22:30:00
33 <NA> NWIS 7 NA 1994-08-25 01:30:00
34 <NA> NWIS 7 NA 1994-08-25 01:30:00
35 <NA> NWIS 7 NA 1994-08-25 22:30:00
36 <NA> NWIS 7 NA 1994-08-25 22:30:00
37 <NA> NWIS 7 NA 1996-04-18 23:00:00
38 <NA> NWIS 7 NA 1996-04-18 18:00:00
39 <NA> NWIS 7 NA 2007-06-27 23:15:00
40 <NA> NWIS 7 NA 2007-06-27 23:15:00
41 <NA> NWIS 7 NA 2009-09-09 22:50:00
42 <NA> NWIS 7 NA 2012-05-17 17:00:00
43 <NA> NWIS 7 NA 2012-06-12 21:00:00
44 <NA> NWIS 7 NA 2012-06-12 21:00:00
45 <NA> NWIS 7 NA 1998-04-22 17:45:00
46 <NA> NWIS 7 NA 1998-04-22 17:45:00
47 <NA> NWIS 7 NA 2012-10-09 18:00:00
48 <NA> NWIS 7 NA 2012-10-09 23:00:00
49 <NA> NWIS 7 NA 2012-10-09 23:00:00
50 <NA> NWIS 7 NA 2012-09-14 18:00:00
51 <NA> NWIS 7 NA 1998-04-22 23:28:00
52 <NA> NWIS 7 NA 1998-04-22 23:28:00
53 <NA> NWIS 7 NA 1998-04-22 21:20:00
54 <NA> NWIS 7 NA 1998-04-22 22:08:00
55 <NA> NWIS 7 NA 1998-04-22 22:08:00
56 <NA> NWIS 7 NA 2005-05-11 21:37:00
57 <NA> NWIS 7 NA 2005-05-05 20:09:00
58 <NA> NWIS 7 NA 2015-05-27 22:50:00
59 <NA> NWIS 7 NA 1998-04-22 19:27:00
60 <NA> NWIS 7 NA 1998-04-22 19:27:00
61 <NA> NWIS 7 NA 2004-05-11 18:20:00
62 <NA> NWIS 7 NA 2004-05-07 20:12:00
63 <NA> NWIS 7 NA 2004-05-07 18:00:00
64 <NA> NWIS 7 NA 2004-08-19 19:10:00
65 <NA> NWIS 7 NA 1998-04-22 21:20:00
66 <NA> NWIS 7 NA 2012-07-18 17:00:00
67 <NA> NWIS 7 NA 2004-05-07 19:55:00
68 <NA> NWIS 7 NA 2005-05-04 20:52:00
69 <NA> NWIS 7 NA 2005-05-17 22:00:00
70 <NA> NWIS 7 NA 2015-06-10 21:20:00
71 <NA> NWIS 7 NA 2005-06-01 18:39:00
72 <NA> NWIS 7 NA 2005-05-16 20:00:00
73 <NA> NWIS 7 NA 1998-04-22 17:26:00
74 <NA> NWIS 7 NA 1998-04-22 17:26:00
75 <NA> NWIS 7 NA 2006-04-27 23:12:00
76 <NA> NWIS 7 NA 2006-04-26 22:30:00
77 <NA> NWIS 7 NA 2006-05-30 19:41:00
78 <NA> NWIS 7 NA 2006-05-03 23:24:00
79 <NA> NWIS 7 NA 2004-05-07 21:40:00
80 <NA> NWIS 7 NA 2015-05-27 20:50:00
81 <NA> NWIS 7 NA 2015-06-17 19:30:00
82 <NA> NWIS 7 NA 2015-05-14 00:40:00
83 <NA> NWIS 7 NA 2015-04-23 00:20:00
84 <NA> NWIS 7 NA 2015-06-03 20:00:00
85 <NA> NWIS 7 NA 2015-06-10 19:00:00
86 <NA> NWIS 7 NA 2012-10-09 18:00:00
87 <NA> NWIS 7 NA 2004-06-01 19:54:00
88 <NA> NWIS 7 NA 2015-06-17 21:20:00
89 <NA> NWIS 7 NA 2015-04-15 23:50:00
90 <NA> NWIS 7 NA 2015-05-20 22:40:00
91 <NA> NWIS 7 NA 2012-09-14 18:00:00
92 <NA> NWIS 7 NA 2006-05-18 22:19:00
93 <NA> NWIS 7 NA 2006-05-31 23:10:00
94 <NA> NWIS 7 NA 2015-06-03 17:20:00
95 <NA> NWIS 7 NA 2004-05-07 20:50:00
96 <NA> NWIS 7 NA 2015-05-06 23:40:00
97 <NA> NWIS 7 NA 2015-04-29 22:20:00
98 <NA> NWIS 8 NA 1998-03-24 18:40:00
99 <NA> NWIS 8 NA 1998-11-09 22:28:00
100 <NA> NWIS 8 NA 1994-11-02 01:20:00
101 <NA> NWIS 8 NA 1994-11-02 01:20:00
102 <NA> NWIS 8 NA 1998-11-09 18:08:00
103 <NA> NWIS 8 NA 1998-02-11 20:55:00
104 <NA> NWIS 8 NA 1998-11-09 22:28:00
105 <NA> NWIS 8 NA 1998-02-11 22:00:00
106 <NA> NWIS 8 NA 1998-02-11 20:55:00
107 <NA> NWIS 8 NA 1998-03-24 18:40:00
108 <NA> NWIS 8 NA 1998-03-24 23:15:00
109 <NA> NWIS 8 NA 1998-03-04 21:02:00
110 <NA> NWIS 8 NA 1998-03-04 21:02:00
111 <NA> NWIS 8 NA 1998-03-05 01:15:00
112 <NA> NWIS 8 NA 1998-11-10 00:20:00
113 <NA> NWIS 8 NA 1998-02-11 22:00:00
114 <NA> NWIS 8 NA 1998-03-05 01:15:00
115 <NA> NWIS 8 NA 1998-11-09 20:30:00
116 <NA> NWIS 8 NA 1998-11-09 20:30:00
117 <NA> NWIS 8 NA 1998-03-24 17:07:00
118 <NA> NWIS 8 NA 1998-03-24 17:07:00
119 <NA> NWIS 8 NA 1998-03-24 17:00:00
120 <NA> NWIS 8 NA 1998-03-24 17:00:00
121 <NA> NWIS 8 NA 1998-11-09 21:44:00
122 <NA> NWIS 8 NA 1998-11-09 21:44:00
123 <NA> NWIS 8 NA 2011-12-29 21:00:00
124 <NA> NWIS 8 NA 2011-12-29 21:00:00
125 <NA> NWIS 8 NA 2011-12-29 21:00:00
126 <NA> NWIS 8 NA 1998-02-11 00:00:00
127 <NA> NWIS 8 NA 1998-02-11 00:00:00
128 <NA> NWIS 8 NA 1998-02-10 18:15:00
129 <NA> NWIS 8 NA 1998-03-04 22:17:00
130 <NA> NWIS 8 NA 1998-03-04 22:17:00
131 <NA> NWIS 8 NA 1998-02-11 19:00:00
132 <NA> NWIS 8 NA 1998-03-05 00:15:00
133 <NA> NWIS 8 NA 1998-03-05 00:15:00
134 <NA> NWIS 8 NA 1998-03-24 23:15:00
135 <NA> NWIS 8 NA 2011-12-29 21:00:00
136 <NA> NWIS 8 NA 1998-03-04 18:02:00
137 <NA> NWIS 8 NA 1998-03-24 22:22:00
138 <NA> NWIS 8 NA 1998-03-24 22:22:00
139 <NA> NWIS 8 NA 1998-11-09 18:08:00
140 <NA> NWIS 8 NA 1998-02-10 18:15:00
141 <NA> NWIS 8 NA 1998-03-24 20:03:00
142 <NA> NWIS 8 NA 1998-03-24 20:03:00
143 <NA> NWIS 8 NA 1998-03-05 02:12:00
144 <NA> NWIS 8 NA 1998-03-05 02:12:00
145 <NA> NWIS 8 NA 1998-02-11 19:00:00
146 <NA> NWIS 8 NA 1998-02-10 20:15:00
147 <NA> NWIS 8 NA 1998-02-10 20:15:00
148 <NA> NWIS 8 NA 1998-03-04 18:02:00
149 <NA> NWIS 8 NA 1998-11-09 23:26:00
150 <NA> NWIS 8 NA 1998-11-09 23:26:00
151 <NA> NWIS 8 NA 1998-11-10 00:20:00
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Let’s explore phosphorus data in Benton County
Code
benton_phos <- readWQPsummary(statecode = "OR",
countycode="Benton",
sampleMedia = "Water",
characteristicName = "Phosphorus")
names(benton_phos) [1] "Provider" "MonitoringLocationIdentifier"
[3] "YearSummarized" "CharacteristicType"
[5] "CharacteristicName" "ActivityCount"
[7] "ResultCount" "LastResultSubmittedDate"
[9] "OrganizationIdentifier" "OrganizationFormalName"
[11] "MonitoringLocationName" "MonitoringLocationTypeName"
[13] "ResolvedMonitoringLocationTypeName" "HUCEightDigitCode"
[15] "MonitoringLocationUrl" "CountyName"
[17] "StateName" "MonitoringLocationLatitude"
[19] "MonitoringLocationLongitude"
Apply some dplyr functions to group site information
Code
benton_phos_summary <- benton_phos |>
rename(Site = MonitoringLocationIdentifier) |>
mutate(Lat = as.numeric(MonitoringLocationLatitude),
Lon = as.numeric(MonitoringLocationLongitude)) |>
group_by(Site, Lat, Lon) |>
summarise(min_year = min(YearSummarized),
max_year = max(YearSummarized),
count = sum(ResultCount)) |>
mutate(POR = max_year - min_year) |>
arrange(desc(count)) |>
ungroup()`summarise()` has grouped output by 'Site', 'Lat'. You can override using the
`.groups` argument.
Code
benton_phos_summary# A tibble: 37 × 7
Site Lat Lon min_year max_year count POR
<chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 USGS-442406123192700 44.4 -123. 2004 2012 11 8
2 USGS-442447123194200 44.4 -123. 2004 2012 11 8
3 USGS-443423123153700 44.6 -123. 2015 2015 11 0
4 USGS-442322123180700 44.4 -123. 1998 1998 10 0
5 USGS-442350123204300 44.4 -123. 1998 1998 10 0
6 USGS-442408123192500 44.4 -123. 1998 1998 10 0
7 USGS-442444123213600 44.4 -123. 1998 1998 10 0
8 USGS-442530123200100 44.4 -123. 1998 1998 10 0
9 USGS-442531123184700 44.4 -123. 1998 1998 10 0
10 USGS-442223123153703 44.4 -123. 1994 1994 8 0
# ℹ 27 more rows
View with mapview- first we need to make our results an sf data frame for mapview to use. Easy!
Code
results_sf <- benton_phos_summary |>
sf::st_as_sf(coords = c("Lon", "Lat"), crs = 4269, remove = FALSE)
mapview(results_sf)NLDI client
The hydro Network-Linked Data Index (NLDI) is a system that can index spatial and river network-linked data and navigate the river network to allow discovery of indexed information.
The NLDI service requires the following:
- A starting entity (
comid,nwis,wqp,locationand more).
- The “more” includes the ever-growing set of resources available in the NLDI service that can be accessed with the
get_nldi_sourcesfunction.
Code
DT::datatable(get_nldi_sources())- A direction to navigate along the network
- UM: upper mainstem
- UT: upper tributary
- DM: downstream mainstem
- DD: downstream divergence
- Features to find along the way!
- Any of the above features (e.g.
get_nldi_sources) - flowlines
- basin
Basic use: NLDI
The NLDI client in dataRetrival operates by speficing the origin, the navigation mode, and the features to find. In the following example, we want to navigate along the upstream tributary of COMID 101 (up to 1000 km) and find all flowline features, nwis gages, and the contributing drainage area.
Code
nldi = findNLDI(comid= 23763529,
nav= "UT",
find= c("flowline", "nwis", 'basin'),
distance_km = 1000)
sapply(nldi, nrow) origin basin UT_nwissite UT_flowlines
1 1 15 187
Code
mapview(nldi)nwmTools
While dataRetrival provides retrieval functions for observed data, there is an increasing amount of simulated data available for use. Once example is the 3 retrospective simulations of the National Water Model. This data certainly falls under the umbrella of “big data” and can be difficult to work with given each hourly file (for up to 42 years!) is stored in separate, cloud based files.
nwmTools provides retrieval functions for these retrospective stream flow data from the NOAA National Water Model reanalysis products:
Retrival
Code
# remotes::install_github("mikejohnson51/nwmTools")
library(nwmTools)
nwis = readNWISdv(site = gsub("USGS-", "", nldi$UT_nwissite$identifier[8]),
param = "00060") |>
renameNWISColumns()
nwm = readNWMdata(comid = nldi$UT_nwissite$comid[8])Aggregation
The timestep of the retrospective NWM data is hourly. In many cases, its more desirable to have the time step aggregated to a different temporal resolution. nwmTools provides a family of aggregation functions that allow you to specify the aggregation time period, and the aggregation summary statistic
Code
grep('aggregate_', ls("package:nwmTools"), value = TRUE) [1] "aggregate_dowy" "aggregate_j" "aggregate_m" "aggregate_record"
[5] "aggregate_s" "aggregate_wy" "aggregate_wym" "aggregate_wymd"
[9] "aggregate_wys" "aggregate_y" "aggregate_yj" "aggregate_ym"
[13] "aggregate_ymd" "aggregate_ys"
Code
# Core function
nwm_ymd = aggregate_ymd(nwm, "mean")
# User defined function
seasonal = aggregate_s(nwm, fun = function(x){quantile(x,.75)})
# Multiple functions
month = aggregate_m(nwm, c('mean', "max"))
a <-ggplot(data = nwis) +
geom_line(aes(x = Date, y = Flow * 0.028316846592)) +
labs(title = "NWIS Data")
b <-ggplot(data = nwm_ymd) +
geom_line(aes(x = ymd, y = flow_cms_v2.1)) +
labs(title = "NWM Daily Average")
c <-ggplot(data = seasonal) +
geom_col(aes(x = season, y = flow_cms_v2.1)) +
labs(title = "NWM Seasonal 75%")
d <-ggplot(data = month) +
geom_col(aes(x = as.factor(month), y = max)) +
geom_col(aes(x = as.factor(month), y = mean), fill = "red") +
labs(title = "NWM Monthly Mean/Maximum")
cowplot::plot_grid(a,b,c,d)opendap.catalog
One of the biggest challenges with Earth System and spatial research is extracting data. These challenges include not only finding the source data but then downloading, managing, and extracting the partitions critical for a given task.
Services exist to make data more readily available over the web but introduce new challenges of identifying subsets, working across a wide array of standards (e.g. non-standards), all without alleviating the challenge of finding resources.
In light of this, opendap.catalog provides 4 primary services.
Learn more about OPenDAP here
Generalized space (XY) and Time (T) subsets for remote and local NetCDF data with dap()
- Handles data streaming, projection, and cropping. Soon to come is masking capabilities.
Code
# remotes::install_github("mikejohnson51/opendap.catalog")
library(opendap.catalog)
Attaching package: 'opendap.catalog'
The following object is masked from 'package:base':
search
Code
AOI = AOI::aoi_get(state = "OR", county = "Benton")
dap <- dap(URL = 'http://cida.usgs.gov/thredds/dodsC/prism',
AOI = AOI,
varname = 'ppt',
startDate = "2005-01-01",
endDate = "2005-01-31")source: http://cida.usgs.gov/thredds/dodsC/prism
varname(s):
> ppt [mm/month] (mean monthly precipitation)
==================================================
diminsions: 19, 12, 2 (names: lon,lat,time)
resolution: 0.042, 0.042, 1 months
extent: -123.83, -123.04, 44.25, 44.75 (xmin, xmax, ymin, ymax)
crs: +proj=longlat +a=6378137 +f=0.00335281066474748 +p...
time: 2005-01-01 to 2005-02-01
==================================================
values: 456 (vars*X*Y*T)
Code
str(dap, max.level = 1)List of 1
$ ppt:S4 class 'SpatRaster' [package "terra"]
Code
plot(terra::rast(dap))A catalog of 14691 web resources (as of 06/2022)
Code
dplyr::glimpse(opendap.catalog::params)Rows: 14,691
Columns: 15
$ id <chr> "hawaii_soest_1727_02e2_b48c", "hawaii_soest_1727_02e2_b48c"…
$ grid_id <chr> "73", "73", "73", "73", "73", "73", "73", "73", "73", "73", …
$ URL <chr> "https://apdrc.soest.hawaii.edu/erddap/griddap/hawaii_soest_…
$ tiled <chr> "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", …
$ variable <chr> "nudp", "nusf", "nuvdp", "nuvsf", "nvdp", "nvsf", "sudp", "s…
$ varname <chr> "nudp", "nusf", "nuvdp", "nuvsf", "nvdp", "nvsf", "sudp", "s…
$ long_name <chr> "number of deep zonal velocity profiles", "number of surface…
$ units <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, …
$ model <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, …
$ ensemble <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, …
$ scenario <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, …
$ T_name <chr> "time", "time", "time", "time", "time", "time", "time", "tim…
$ duration <chr> "2001-01-01/2022-01-01", "2001-01-01/2022-01-01", "2001-01-0…
$ interval <chr> "365 days", "365 days", "365 days", "365 days", "365 days", …
$ nT <dbl> 22, 22, 22, 22, 22, 22, 22, 22, 22, 22, 22, 22, 22, 22, 22, …
With 14,160 web resources documented, there are simply too many resources to search through by hand unless you know exactly what you want. This voids the possibility of serendipitous discovery. So, we have added a generally fuzzy search tool to help discover datasets.
Say you want to find what resources there are for daily rainfall? search and search_summary can help:
Code
search("daily precipitation") |>
search_summary() |>
DT::datatable()Code
(s = search('gridmet daily precipitation'))# A tibble: 1 × 16
id grid_id URL tiled variable varname long_name units model ensemble
<chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr>
1 gridmet 176 http://… "" pr precip… pr mm <NA> <NA>
# ℹ 6 more variables: scenario <chr>, T_name <chr>, duration <chr>,
# interval <chr>, nT <dbl>, rank <dbl>
Pass catalog elements to the generalized toolsets:
Now that you know the precipitation product you want, lets say we want to look at precipitation in Benton County in April 2022:
Code
(s = opendap.catalog::search('gridmet daily precipitation'))# A tibble: 1 × 16
id grid_id URL tiled variable varname long_name units model ensemble
<chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr>
1 gridmet 176 http://… "" pr precip… pr mm <NA> <NA>
# ℹ 6 more variables: scenario <chr>, T_name <chr>, duration <chr>,
# interval <chr>, nT <dbl>, rank <dbl>
Code
(OR_rain = dap(catalog = s,
AOI = AOI::aoi_get(state = "OR"),
startDate = "2022-04-01",
endDate = "2022-09-30"))source: http://thredds.northwestknowledge.net:8080/thredds/dodsC/agg...
varname(s):
> precipitation_amount [mm] (pr)
==================================================
diminsions: 198, 105, 183 (names: lon,lat,day)
resolution: 0.042, 0.042, 1 days
extent: -124.7, -116.45, 41.96, 46.34 (xmin, xmax, ymin, ymax)
crs: +proj=longlat +a=6378137 +f=0.00335281066474748 +p...
time: 2022-04-01 to 2022-09-30
==================================================
values: 3,804,570 (vars*X*Y*T)
$precipitation_amount_total
class : SpatRaster
dimensions : 105, 198, 183 (nrow, ncol, nlyr)
resolution : 0.04166667, 0.04166667 (x, y)
extent : -124.7042, -116.4542, 41.9625, 46.3375 (xmin, xmax, ymin, ymax)
coord. ref. : +proj=longlat +ellps=WGS84 +no_defs
source(s) : memory
names : 2022-04-01, 2022-04-02, 2022-04-03, 2022-04-04, 2022-04-05, 2022-04-06, ...
min values : 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...
max values : 39.4, 55.4, 111.3, 76.5, 16.5, 8.9, ...
unit : mm, mm, mm, mm, mm, mm, ...
Code
{
plot(sum(OR_rain[[1]]) * 0.001,
main = "Meters of rainfall: Oregon April 2022")
}Tiled data streams
One of the key features of dap is the capacity to make requests over tiled resources. Some resources (like MODIS) are tiled in space, and some (like MACA, and LOCA) are tiled in time. Below we see an example of how a single dap call can consolidate a request that covers multiple spatial tiles:
This one isn’t working at the moment - not sure why…
Code
(s = opendap.catalog::search('mod16a2v006 PET'))# A tibble: 1 × 16
id grid_id URL tiled variable varname long_name units model ensemble
<chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr>
1 MOD16A2.0… <NA> http… XY_m… <NA> PET_50… MODIS Gr… kg/m… <NA> <NA>
# ℹ 6 more variables: scenario <chr>, T_name <chr>, duration <chr>,
# interval <chr>, nT <dbl>, rank <dbl>
Code
# dap = dap(
# catalog = s,
# AOI = AOI::aoi_get(state = "OR", county = "Benton"),
# startDate = "2010-01-01",
# endDate = "2010-01-31"
# )
#
# plot(dap)Zonal analysis
Often out goal when acquiring spatial dat is producing area-based summaries, particularly for watershed analyses.
zonal is a package that wraps the excellent exactextractr package with some added performance and convenience for these types of tasks.
To use zonal, you must supply:
- a raster dataset (SpatRaster)
- a geometry set to summarize over
- a summary function (similar to
nwmTools::aggregate_*) - The column with a unique identifier for each polygon object, and whether the summary data should be joined (
join = TRUE) to the original summary data.
Code
# remotes::install_github("NOAA-OWP/zonal")
library(zonal)
# Summary options
zonal::ee_functions() [1] "min" "max"
[3] "count" "sum"
[5] "mean" "median"
[7] "quantile" "mode"
[9] "majority" "minority"
[11] "variety" "variance"
[13] "stdev" "coefficient_of_variation"
[15] "weighted_mean" "weighted_sum"
[17] "frac" "weighted_frac"
Code
zonal::weight_functions() collapse base
1 fmean mean
2 fmedian median
3 fmode mode
4 fsum sum
5 fprod prod
6 fsd sd
7 fvar var
8 fmin min
9 fmax max
10 fnth nth
11 ffirst first
12 flast last
13 fnobs nobs
14 fndistinct ndistinct
Basic Use
Code
library(zonal)
OR_counties <- AOI::aoi_get(state = "OR", county = "all")
system.time({
summary_data = execute_zonal(OR_rain,
geom = OR_counties,
fun = "sum",
ID = "fip_code",
join = TRUE)
}) user system elapsed
0.47 0.04 0.50
Code
plot(summary_data[grepl("sum", names(summary_data))], border = NA,
max.plot = 20)Web data without a “service”
Often the needed data is not delivered by a service or function. This does not mean we are out of luck, but rather that we have to do a little of the “heavy lifting” ourselves. This final section highlights how to vector and raster data from a URL using the http/https and s3 protocols.
These approaches are advantageous when you don’t want to download data locally for analysis.
Access Raster Data
GDAL provides a number of Virtual File System drivers to read compressed and network data.
Three of the most useful for common spatial data tasks include:
vsizip –> file handler that allows reading ZIP archives on-the-fly without decompressing them beforehand.
vsicurl –> A generic file system handler exists for online resources that do not require particular signed authentication schemes
vsis3 –> specialized into sub-filesystems for commercial cloud storage services (e.g. AWS, also see /vsigs/, /vsiaz/, /vsioss/ or /vsiswift/).
Basic Usgage
Duke store VRT files for the POLARIS soils dataset. terra can generate pointers to these datasets - however data is not extracted until an operation is called on the data. As seen above dap provides simplified access to extract subsets of data for OpenDap and VRT endpoints.
Code
terra::rast('/vsicurl/http://hydrology.cee.duke.edu/POLARIS/PROPERTIES/v1.0/vrt/alpha_mean_0_5.vrt') class : SpatRaster
dimensions : 97200, 212400, 1 (nrow, ncol, nlyr)
resolution : 0.0002777778, 0.0002777778 (x, y)
extent : -125, -66, 23, 50 (xmin, xmax, ymin, ymax)
coord. ref. : lon/lat WGS 84 (EPSG:4326)
source : alpha_mean_0_5.vrt
name : alpha_mean_0_5
Code
opendap.catalog::dap('/vsicurl/http://hydrology.cee.duke.edu/POLARIS/PROPERTIES/v1.0/vrt/alpha_mean_0_5.vrt', AOI = AOI::aoi_get(state = "OR", county = "Benton")) |>
plot()Transect example
Here is an example where a global elevation dataset can quickly be used in conjunction with the NLDI client to plot the elevation transect of a the Calapooyia River near here:
Code
d = dataRetrieval::findNLDI(loc = geocode("Pioneer Park Brownsville, Oregon", pt = TRUE),
nav = "UM",
find = c("flowlines", "basin"))
mapview::mapview(d)Code
COP30 = opendap.catalog::dap("/vsicurl/https://opentopography.s3.sdsc.edu/raster/COP30/COP30_hh.vrt",
AOI = d$basin)
transect = terra::extract(COP30, terra::vect(sf::st_union(d$UM_flowlines))) |>
dplyr::mutate(location = c(n():1))
ggplot2::ggplot(transect, aes(x = location, y = COP30_hh)) +
geom_line() +
geom_smooth()`geom_smooth()` using method = 'gam' and formula = 'y ~ s(x, bs = "cs")'
Access Vector Data
Basic Usgage: TIGER lines Since the base data reader of sf is GDAL the vsi capabilities also apply to vector data! Note that the tigris and tidycensus packages are the recommended way to access US Census data, this is simply by way of example. The added challenge typically is that vector data (especially shapefile data) is generally stored in zip files. This adds an extra layer of complexity when remotely reading these datasets.
In this example we look at the US Census Bureaus FTP server for TIGER road lines. The data is stored by by 5 digit (county) FIP code in zip files. To access this data we need to identify the FIP code of interest and then chain a vsizip with a vsicurl call.
Code
AOI = aoi_get(state = "OR", county = "Benton")
file = paste0('tl_2021_',AOI$fip_code, '_roads')
url = paste0('/vsizip/{/vsicurl/https://www2.census.gov/geo/tiger/TIGER2021/ROADS/', file, '.zip}/',file,'.shp')
system.time({
roads = sf::read_sf(url)
}) user system elapsed
0.22 0.04 4.09
Code
mapview::mapview(roads)For those curious about the generalization of this, we can see that any GDAL backed software (here terra) can utilize the same URL:
Code
system.time({
roads2 = terra::vect(url)
}) user system elapsed
0.25 0.03 1.29
Code
plot(roads2)Use Cases
Hydro Addressing
Finding where along a river system some spatial data lies is a key use case for many modeling and analysis tasks. A full discussion is available in the nhdplusTools indexing and referencing vignette.
We need two inputs. Lines to index to and points that we want addresses for. With these inputs, nhdplusTools (and soon hydroloom) supports generation of hydro addresses with get_flowline_index().
The example below is as simple as possible. get_flowline_index() has a number of other capabilities, such as increased address precision and the ability to return multiple nearby addresses, that can be found in the function documentation.
Code
source(system.file("extdata/new_hope_data.R", package = "nhdplusTools"))
fline <- sf::st_transform(sf::st_cast(new_hope_flowline, "LINESTRING"), 4326)
point <- sf::st_sfc(sf::st_point(c(-78.97, 35.916)),
crs = 4326)
(address <- nhdplusTools::get_flowline_index(fline, point)) id COMID REACHCODE REACH_meas offset
1 1 8894158 03030002000064 58.0596 0.0002283261
Code
plot(sf::st_geometry(fline[fline$COMID %in% address$COMID,]))+
plot(point, add = TRUE)integer(0)
A natural next step once we’ve found a hydro address is to search upstream or downstream from the location we found. nhdplusTools and soon hydroloom offer a few upstream / downstream search functions. Here we’ll show a couple from nhdplusTools.
Code
up <- nhdplusTools::get_UT(fline, address$COMID)
um <- nhdplusTools::get_UM(fline, address$COMID)
dn <- nhdplusTools::get_DD(fline, address$COMID)
dm <- nhdplusTools::get_DM(fline, address$COMID)
plot(sf::st_geometry(fline), col = "grey", lwd = 0.5)
plot(sf::st_geometry(fline[fline$COMID %in% c(up, dn),]),
add = TRUE)
plot(sf::st_geometry(fline[fline$COMID %in% c(um, dm),]),
col = "blue", lwd = 2, add = TRUE)
plot(point, cex = 2, lwd = 2, add = TRUE)The ability to navigate up or down a mainstem rather than all connected tributaries and diversions, requires some attributes that identify primary up and downstream paths. Without those paths, navigation is still possible but the “main” path navigation method is not. Below, a new function available in hydroloom, navigate_network_dfs() is shown.
Code
# remotes::install_github("DOI-USGS/nhdplusTools@2cb81da")
#| warning: false
net <- hydroloom::add_toids(sf::st_drop_geometry(fline),
return_dendritic = FALSE)
up <- hydroloom::navigate_network_dfs(net, address$COMID, direction = "up")
dn <- hydroloom::navigate_network_dfs(net, address$COMID, direction = "dn")
plot(sf::st_geometry(fline), col = "grey", lwd = 0.5)
plot(sf::st_geometry(fline[fline$COMID %in% c(unlist(up), unlist(dn)),]),
add = TRUE)
plot(point, cex = 2, lwd = 2, add = TRUE)Catchment Characteristics and Accumulation
nhdplusTools (only in the latest hydroloom branch) and StreamCatTools can be used to retrieve a wide range of catchment characteristics. See the StremCatTools Vignette for further details and examples.
Code
library(nhdplusTools)
# remotes::install_github("USEPA/StreamCatTools")
library(StreamCatTools)
nhdplusTools::nhdplusTools_data_dir(tempdir())
cat <- sf::st_transform(new_hope_catchment, 4326)
streamcat_chars <- StreamCatTools::sc_get_params(param='name')
nawqa_chars <- nhdplusTools::get_characteristics_metadata()
StreamCatTools::sc_fullname("pctimp2016")[1] "Mean Imperviousness 2016"
Code
nawqa_chars$description[nawqa_chars$ID == "CAT_TWI"][1] "Topographic wetness index, ln(a/S); where ln is the natural log, a is the upslope area per unit contour length and S is the slope at that point. See http://ks.water.usgs.gov/Kansas/pubs/reports/wrir.99-4242.html and Wolock and McCabe, 1995 for more detail"
Code
twi <- nhdplusTools::get_catchment_characteristics("CAT_TWI", cat$FEATUREID)
twi <- dplyr::rename(twi, CAT_TWI = "characteristic_value")
imp <- StreamCatTools::sc_get_data("pctimp2016", 'catchment', cat$FEATUREID)
cat <- dplyr::left_join(cat, imp, by = c("FEATUREID" = "COMID"))
# dplyr::left_join(twi, by = c("FEATUREID" = "comid"))
# plot(cat['CAT_TWI'])
plot(cat['PCTIMP2016CAT'])With the two catalogs of characteristics above, we have access to nearly any characteristic imaginable and both even offer downstream accumulations of the variables. However, sometimes it is necessary to sample and accumulate data not already available. nhdplusTools supports accumulating drainage area and length and hydroloom, going forward, supports downstream accumulation more generically. Here, we’ll just use one of the examples pulled above to illustrate how this works.
Code
library(dplyr)
accum_df <- sf::st_drop_geometry(fline) |>
dplyr::select(COMID, FromNode, ToNode, Divergence, AreaSqKM) |>
hydroloom::add_toids() |>
dplyr::left_join(dplyr::select(cat, -AreaSqKM), by = c("COMID" = "FEATUREID")) |>
sf::st_sf() |>
dplyr::mutate(area_weighted_PCTIMP2016CAT =
ifelse(is.na(PCTIMP2016CAT) & AreaSqKM == 0,
yes = 0, no = AreaSqKM * PCTIMP2016CAT))
accum_df <- accum_df |>
dplyr::mutate(tot_PCTIMP2016CAT =
hydroloom::accumulate_downstream(accum_df, "area_weighted_PCTIMP2016CAT") /
hydroloom::accumulate_downstream(accum_df, "AreaSqKM"))
plot(cat['PCTIMP2016CAT'])Code
plot(accum_df['tot_PCTIMP2016CAT'])R and Python Interoperability
We can usReticulate to inter-operate easily between R and Python within RStudio and share objects between languages
Code
library(reticulate)
# reticulate::install_miniconda()
# create a new environment conda environment
# conda_create("r-reticulate")
# install NumPy and SciPy
# conda_install("r-reticulate", "scipy")
# conda_install("r-reticulate", "numpy")
# conda_install("r-reticulate", "pandas")
# indicate that we want to use a specific condaenv
use_condaenv("r-reticulate")
np <- import("numpy")
print(np$version$full_version)[1] "1.24.3"
Simple examples from reticulate - calling Python:
Import a module and call a function from Python:
Code
os <- import("os")
os$listdir(".") [1] ".git" ".gitignore"
[3] ".ipynb_checkpoints" ".Rhistory"
[5] ".Rproj.user" "3dep.ipynb"
[7] "cache" "classification.ipynb"
[9] "Dam_Impact.html" "Dam_Impact.ipynb"
[11] "Dam_Impact.Rmd" "environment.yml"
[13] "ICRW8_Geospatial_Workshop.Rproj" "img"
[15] "README.md" "Relative_Elevation_Model.ipynb"
[17] "Slides.html" "Slides.ipynb"
[19] "Slides.qmd" "Slides.rmarkdown"
[21] "Slides_files"
Object conversion - When Python objects are returned to R they are converted to R objects by default - but you can deal in native Python types by choosing convert=FALSE in the import function
Code
# import numpy and specify no automatic Python to R conversion
np <- import("numpy", convert = FALSE)
# do some array manipulations with NumPy
a <- np$array(c(1:4))
print (a)array([1, 2, 3, 4])
Code
sum <- a$cumsum()
# convert to R explicitly at the end
print (py_to_r(a))[1] 1 2 3 4
Code
py_to_r(sum)[1] 1 3 6 10
Passing Dataframes between Python and R:
Code
pd <- import("pandas")
penguins <- pd$read_csv("https://vincentarelbundock.github.io/Rdatasets/csv/modeldata/penguins.csv")
dplyr::glimpse(penguins)Rows: 344
Columns: 8
$ `Unnamed: 0` <dbl> 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 1…
$ species <chr> "Adelie", "Adelie", "Adelie", "Adelie", "Adelie", "A…
$ island <chr> "Torgersen", "Torgersen", "Torgersen", "Torgersen", …
$ bill_length_mm <dbl> 39.1, 39.5, 40.3, NaN, 36.7, 39.3, 38.9, 39.2, 34.1,…
$ bill_depth_mm <dbl> 18.7, 17.4, 18.0, NaN, 19.3, 20.6, 17.8, 19.6, 18.1,…
$ flipper_length_mm <dbl> 181, 186, 195, NaN, 193, 190, 181, 195, 193, 190, 18…
$ body_mass_g <dbl> 3750, 3800, 3250, NaN, 3450, 3650, 3625, 4675, 3475,…
$ sex <list> "male", "female", "female", NaN, "female", "male", …
Or like this:
Code
import pandas as pd
penguins =pd.read_csv("https://vincentarelbundock.github.io/Rdatasets/csv/modeldata/penguins.csv")
print(penguins.head()) Unnamed: 0 species island ... flipper_length_mm body_mass_g sex
0 1 Adelie Torgersen ... 181.0 3750.0 male
1 2 Adelie Torgersen ... 186.0 3800.0 female
2 3 Adelie Torgersen ... 195.0 3250.0 female
3 4 Adelie Torgersen ... NaN NaN NaN
4 5 Adelie Torgersen ... 193.0 3450.0 female
[5 rows x 8 columns]
Call Python from R:
Code
penguins <- py$penguins
dplyr::glimpse(penguins)Rows: 344
Columns: 8
$ `Unnamed: 0` <dbl> 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 1…
$ species <chr> "Adelie", "Adelie", "Adelie", "Adelie", "Adelie", "A…
$ island <chr> "Torgersen", "Torgersen", "Torgersen", "Torgersen", …
$ bill_length_mm <dbl> 39.1, 39.5, 40.3, NaN, 36.7, 39.3, 38.9, 39.2, 34.1,…
$ bill_depth_mm <dbl> 18.7, 17.4, 18.0, NaN, 19.3, 20.6, 17.8, 19.6, 18.1,…
$ flipper_length_mm <dbl> 181, 186, 195, NaN, 193, 190, 181, 195, 193, 190, 18…
$ body_mass_g <dbl> 3750, 3800, 3250, NaN, 3450, 3650, 3625, 4675, 3475,…
$ sex <list> "male", "female", "female", NaN, "female", "male", …
Call R from Python:
Code
data(iris)
iris <- iris |>
dplyr::filter(Petal.Length > 3)Code
print(r.iris.head()) Sepal.Length Sepal.Width Petal.Length Petal.Width Species
0 7.0 3.2 4.7 1.4 versicolor
1 6.4 3.2 4.5 1.5 versicolor
2 6.9 3.1 4.9 1.5 versicolor
3 5.5 2.3 4.0 1.3 versicolor
4 6.5 2.8 4.6 1.5 versicolor
Code
print(r.iris['Petal.Length'].min())3.3
Resources
General
R
- Hydroinformatics in R: Extensive Notes and exercises for a course on data analysis techniques in hydrology using the programming language R
- Spatial Data Science by Edzar Pebesma and Roger Bivand
- Geocomputation with R
- r-spatial: Suite of fundamental packages for working with spatial data in R
- Working with Geospatial Hydrologic Data Using Web Services (R)
- Accessing REST API (JSON data) using httr and jsonlite
Python
- Datashader: Accurately render even the largest data
- GeoPandas
- HyRiver: a suite of Python packages that provides a unified API for retrieving geospatial/temporal data from various web services
- Python Foundation for Spatial Analysis
- Python for Geographic Data Analysis
- gdptools A Python package for grid- or polygon-polygon area-weighted interpolation statistics
- Intro to Python GIS
- xarray: An open-source project and Python package that makes working with labeled multi-dimensional arrays simple, efficient, and fun!
- rioxarray: Rasterio xarray extension.
- GeoPandas: An open-source project to make working with geospatial data in python easier.
- OSMnx: A Python package that lets you download and analyze geospatial data from OpenStreetMap.
- Xarray Spatial: Implements common raster analysis functions using
numbaand provides an easy-to-install, easy-to-extend codebase for raster analysis.